Advanced NMR and MS based metabolomics in cancer biomarker discovery

Siwei Wei, Purdue University

Abstract

As a promising and fast growing field in systems biology, metabolomics is being applied to diverse areas such as disease detection, nutrition, toxicity, and systems biology. Metabolomics combines data from the high resolution analytical techniques of nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) with multivariate statistical analysis to facilitate information recovery from the complex biological samples and to develop predictive models for a number of applications.^ In this thesis we first present a new methodological approach, RANSY (Ratio Analysis NMR SpectroscopY), which identifies all the peaks of a specific metabolite based on the ratios of peak heights or integrals. RANSY outperforms other methods used for spectral identification in intact complex samples. This approach could aid in the identification of unknown metabolites using 1D or 2D NMR spectra in virtually any complex biological mixture.^ The second part of this thesis focuses on cancer biomarker discovery. We investigated serum metabolite profiles in breast cancer patients using NMR and liquid chromatography (LC-MS) based metabolomics. We found 11 metabolites that distinguish breast cancer patients from controls; when combined in a model, these metabolites yielded 94% sensitivity in detecting the breast cancer patients. We also performed metabolic profiling using a combination of NMR and LC-MS of serum from breast cancer patients with complete, partial and no response to chemotherapy. A prediction model developed using four metabolites correctly identified 80% of the patients that did not show complete response to chemotherapy. This result shows promise for developing more personalized treatment protocols for breast cancer patients. Finally, metabolic profiling of serum samples from patients with hepatocellular carcinoma (HCC) and heptatitis C virus (HCV) was performed by 1H NMR. A partial least squares discriminant analysis model based on these three metabolites provided an overall accuracy of 83%, sensitivity of 80% and specificity of 71%, outperforming the gold standard clinical marker alpha-fetoprotein. This methodology could provide an alternative approach for HCC screening in HCV patients, who have high risk for developing liver cancer.^

Degree

Ph.D.

Advisors

Daniel Raftery, Purdue University.

Subject Area

Women's studies|Analytical chemistry|Oncology

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