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The discovery of tyrosine kinase inhibitors (TKIs) brought a major breakthrough in the treatment of patients with chronic myeloid leukemia (CML). Pathogenetic CML events are closely linked with the Bcr-Abl protein with tyrosine kinase activity. TKIs block the ATP-binding site; therefore, the signal pathways leading to malignant transformation are no longer active. However, there is limited information about the impact of TKI treatment on the metabolome of CML patients. Using liquid chromatography mass spectrometric metabolite profiling and multivariate statistical methods, we analyzed plasma and leukocyte samples of patients newly diagnosed with CML, patients treated with hydroxyurea and TKIs (imatinib, dasatinib, nilotinib), and healthy controls. The global metabolic profiles clearly distinguished the newly diagnosed CML patients and the patients treated with hydroxyurea from those treated with TKIs and the healthy controls. The major changes were found in glycolysis, the citric acid cycle, and amino acid metabolism. We observed differences in the levels of amino acids and acylcarnitines between those patients responding to imatinib treatment and those who were resistant to it. According to our findings, the metabolic profiling may be potentially used as an additional tool for the assessment of response/resistance to imatinib.

IntroductionOne of the body fluids often used in metabolomics studies is urine. The concentrations of metabolites in urine are affected by hydration status of an individual, resulting in dilution differences. This requires therefore normalization of the data to correct for such differences. Two normalization techniques are commonly applied to urine samples prior to their further statistical analysis. First, AUC normalization aims to normalize a group of signals with peaks by standardizing the area under the curve (AUC) within a sample to the median, mean or any other proper representation of the amount of dilution. The second approach uses specific end-product metabolites such as creatinine and all intensities within a sample are expressed relative to the creatinine intensity.ObjectivesAnother way of looking at urine metabolomics data is by realizing that the ratios between peak intensities are the information-carrying features. This opens up possibilities to use another class of data analysis techniques designed to deal with such ratios: compositional data analysis. The aim of this paper is to develop PARAFAC modeling of three-way urine metabolomics data in the context of compositional data analysis and compare this with standard normalization techniques.MethodsIn the compositional data analysis approach, special coordinate systems are defined to deal with the ratio problem. In essence, it comes down to using other distance measures than the Euclidian Distance that is used in the conventional analysis of metabolomic data.ResultsWe illustrate using this type of approach in combination with three-way methods (i.e. PARAFAC) of a longitudinal urine metabolomics study and two simulations. In both cases, the advantage of the compositional approach is established in terms of improved interpretability of the scores and loadings of the PARAFAC model.ConclusionFor urine metabolomics studies, we advocate the use of compositional data analysis approaches. They are easy to use, well established and proof to give reliable results.