Author of

• 11543

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  P2.06 - Poster Session 2 - Prognostic and Predictive Biomarkers (ID 165)

  • Event: WCLC 2013
  • Type: Poster Session
  • Track: Biology
  • Presentations: 1
  • Moderators:
  • Coordinates: 10/29/2013, 09:30 - 16:30, Exhibit Hall, Ground Level

  • 11572

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    P2.06-029 - Alterations detected in glycerophospholipid profile of lung cancer tissue samples by mass spectrometry (ID 2417)

    09:30 - 09:30  |  Author(s): T. Laisaar
    • Abstract

    Background
    Metabolomics is an approach that is increasingly used in research of different types of cancer as it reflects the most proximate deviations in metabolism of cells and tissues. It has been shown before that untargeted metabolomics can effectively distinguish diseased tissue samples from healthy ones and diseased individuals from healthy ones regarding the general metabolite pattern of the samples.

    Methods
    In this study lung cancer tissue and macroscopically non-cancer tissue were obtained from lung cancer patients (n=45) and analysed using liquid chromatography combined with tandem mass spectrometry to acquire full spectra of metabolites between mass-to-charge ratio (m/z) 50-1000 extracted in hydrophobic and hydrophilic phase in positive and negative ionisation mode. Principal component analysis (PCA), sparse partial least squares discriminant analysis (SPLS-DA), paired t-test were used for data analysis. Fragmentation and comparison of fragment spectra to public and in-house databases were used for metabolite identification.

    Results
    PCA and SPLS-DA indicated the existence of systematic differences in between the two types of tissue based on the data matrix extracted from mass-spectra. All together 496 signals (13% of all measured signals) were found to have significant (p<0.001) alteration by t-test between cancer and non-cancer spectra. Eleven most prominent of them were selected for fragmentation. Their mass-to-charge ratios (m/z) were in hydrophilic phase as follows: +86, -147, +168, -171, -201, and in hydrophobic phase +735, +736, -774, -775, -888 and -889. Signals with m/z of -171, +735, +736, -774 and -775 were found more abundant in non-cancer tissue and the signals with m/z of +86, +168, -147, -201, -888 and -889 in the cancer tissue compared to non-cancer tissue. From hydrophobic phase signals +735 and +736 were identified as unspecified species of phosphatidylcholines. M/z -888 was identified as phosphatidylinositol 18:0-20:3 (stearic acid and Mead acid as lipid acid residues) having on average two-fold stronger signal in the spectra of cancer tissue compared to non-cancer tissue. The phosphatidylinositols are, in addition to structural purposes in membranes, known to be used as a depot for PUFA-s, such as arachidonic acid (AA; 20:4). As the increase of free Mead acid in blood has been linked to the deficiency of essential fatty acids (as AA) in food, we suggest our finding indicates that lung cancer cells have significantly increased demand for inflammatory mediators synthesized from AA.

    Conclusion
    Cancer tissue is clearly distinguishable from non-cancer tissue with mass spectrometry. From all low-molecular weight metabolites 13% are differing in cancerous and non-cancer tissue. Most striking changes were assigned to phospholipids and in particular to Mead acid containing phosphatidylinositols, which are upregulated in cancer tissues by 100%. The results imply differential regulation of polyunsaturated fatty acids in lung cancer, which improves our knowledge of molecular mechanisms of cancer and opens new ways for accurate prognostic markers and personalized approach to postoperative treatment.