Optimizing the Use of Quality Control Samples for Signal Drift Correction in Large-Scale Urine Metabolic Profiling Studies.

Analytical Chemistry (Impact Factor: 5.83). 02/2012;
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    Analytical and Bioanalytical Chemistry 03/2013; · 3.66 Impact Factor
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    ABSTRACT: Cancer is not only a complex genetic disease, but also a disease of dysregulated bioenergetic metabolism. With improved technological advancements, the focus has shifted from changes in an individual biochemical pathway or metabolite toward changes in the context of the global network of metabolic pathways in a cell, tissue or organism. This global approach allows identifying changes in the pattern of metabolite expression in addition to changes in individual metabolite or pathway. Such a metabolomics approach promises a better understanding of tumor biology and identification of potential biomarkers with applications as diagnostic, prognostic and therapeutic targets. In this review, we discuss various techniques used in metabolomics and analysis of the data generated and its specific uses in cancer research including novel biomarker identification, development of more sensitive and specific diagnostic methods, monitoring of currently used cancer therapeutics to evaluate the prognostic outcome with a given therapy and evaluating novel therapeutic strategies.
    Expert Review of Proteomics 08/2013; 10(4):325-36. · 3.90 Impact Factor
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    ABSTRACT: Differences in sample collection, biomolecule extraction, and instrument variability introduce bias to data generated by liquid chromatography coupled with mass spectrometry (LC-MS). Normalization is used to address these issues. In this paper, we introduce a new normalization method using the Gaussian process regression model (GPRM) that utilizes information from individual scans within an extracted ion chromatogram (EIC) of a peak. The proposed method is particularly applicable for normalization based on analysis order of LC-MS runs. Our method uses measurement variabilities estimated through LC-MS data acquired from quality control samples to correct for bias caused by instrument drift. Maximum likelihood approach is used to find the optimal parameters for the fitted GPRM. We review several normalization methods and compare their performance with GPRM. To evaluate the performance of different normalization methods, we consider LC-MS data from a study where metabolomic approach is utilized to discover biomarkers for liver cancer. The LC-MS data were acquired by analysis of sera from liver cancer patients and cirrhotic controls. In addition, LC-MS runs from a quality control (QC) sample are included to assess the run to run variability and to evaluate the ability of various normalization method in reducing this undesired variability. Also, ANOVA models are applied to the normalized LC-MS data to identify ions with intensity measurements that are significantly different between cases and controls. One of the challenges in using label-free LC-MS for quantitation of biomolecules is systematic bias in measurements. Several normalization methods have been introduced to overcome this issue, but there is no universally applicable approach at the present time. Each data set should be carefully examined to determine the most appropriate normalization method. We review here several existing methods and introduce the GPRM for normalization of LC-MS data. Through our in-house data set, we show that the GPRM outperforms other normalization methods considered here, in terms of decreasing the variability of ion intensities among quality control runs.
    Proteome Science 11/2013; 11(Suppl 1):S13. · 1.88 Impact Factor