Ovarian cancer is the fifth most common cancer among women, with a great demand for the development of diagnostics towards improved performance, shorter turnaround time and lower costs. Lipids represent an important class of biomolecules with a unique but nevertheless poorly understood role in cancer pathophysiology. DESI-MSI (Desorption electrospray ionization mass spectrometric imaging) is an emerging mass spectrometric technique with great potential in cancer diagnostics and prognostics, giving spatially resolved profiling information. These techniques provide information on the metabolic and lipidomic profile of tissues, giving insight into tumour systems biochemistry.
The hypothesis of this study is that spatially resolved molecular phenotypes (i.e. distribution of lipid species in the tissue) measured by modern mass spectrometry technologies can capture systems biochemistry in different subtypes of ovarian cancer. To validate this hypothesis epithelial ovarian carcinoma samples (with a total of 112 samples including different epithelial ovarian carcinomas and healthy ovary) were analysed by DESI - MSI. With the histologically resolved lipidomic information given by DESI, multivariate statistical analysis was performed. The analysis of the spectral content revealed changes in the relative abundance of different classes of phospholipids as well as changes inside the class related to the fatty acid composition of these lipid species.
A semi-targeted LC-MS method for phospholipids and sphingolipids was developed to be used for the analysis of the ovarian sample set in order to refine the findings of the DESI-MSI study. The DESI stage was also coupled with a triple quadrupole to optimize a targeted analysis with spatial information.
In conclusion DESI MS was successfully used to analyse the lipidome of epithelial ovarian carcinoma. This analysis allowed us to better characterise the metabolic variations within and between histological groups and possibly the beginning of a new tool for a more precise epithelial ovarian cancer diagnosis and prognosis.