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Using Comparative Metabolomics to Target Cancer
Author(s) -
Griffin Jules
Publication year - 2015
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.29.1_supplement.367.2
Subject(s) - metabolomics , metabolome , computational biology , lipid metabolism , proteomics , biology , lipidomics , chemistry , in vivo , cell growth , eicosanoid metabolism , cancer research , biochemistry , bioinformatics , eicosanoid , microbiology and biotechnology , arachidonic acid , gene , enzyme
Because of the complexities and diversity of metabolites, no one analytical approach can cover the whole metabolome. We have been using a range of analytical platforms based around a combination of NMR spectroscopy and mass spectrometry to maximise the coverage of the metabolome and then map these changes onto other datasets such as MRI images, transcriptomics and proteomics using multivariate statistics in order to build up a profile of tumour cell metabolism. Such data fusion based approaches provide increased mechanistic insight into the development of cancer. I will present examples from: (i) apoptosis in glioma associated with the accumulation of polyunsaturated fatty acids, (ii) proliferation and aggressiveness in breast cancer characterised by increased turnover in newly synthesized phosphatidylcholines and (iii) the development of non‐genotoxic carcinogenicity in liver tissue associated with alterations in eicosanoid metabolism. In particular, I will focus on how cell proliferation and apoptosis is associated with key changes in lipid metabolism, helping us understand the metabolic changes detectable by magnetic resonance spectroscopy and providing a mechanistic framework for some of the in vivo biomarker associated with tumour detection and treatment.