Characterization of extracellular vesicles from ascitic fluid in ovarian cancer patients - Rodrigues, Leandra José Silva (2022)
Ovarian cancer is the first cause of death among gynaecological malignancies in women. Most patients are diagnosed at late stages and present ascites, the accumulation of ascitic fluid in the perito- neal cavity, associated to metastasis and poor prognosis. Ascitic fluids are enriched in pro-tumorigenic components, such as extracellular vesicles (EVs) involved in the tumour microenvironment intercellular crosstalk. Despite metabolic alterations being a hallmark of cancer and highly correlated with sensitivity to treatment, the metabolite cargo is still the least explored component of EVs in ovarian cancer. The aim of this thesis was to characterize the metabolic signature of EVs isolated from ascitic fluids in ovarian cancer patients. Methods for the characterization of EVs were first implemented using EVs isolated from an ovarian cancer cell line (ES-2), namely for the presence of EV and non-EV com- ponents by Western blot, as well as the analytical platform for metabolomics and lipidomics by LC- MS. Ultimately, EVs were isolated using differential centrifugation from ovarian cancer biofluids, in- cluding ascitic fluids and peritoneal washes as controls. Isolated and discarded fractions were charac- terized by Western blot; by transmission electron microscopy (TEM) for morphology, and by nanopar- ticle tracking analysis (NTA) for particle concentration and size, following the International Society for Extracellular Vesicles (ISEV) guidelines. We demonstrated the recovery of EVs and increasing purity of isolates and confirmed the pres- ence of EVs on biofluids. The metabolic characterization of ES-2 EVs suggested an enrichment in metabolic pathways involved in cancer processes. This methodology for characterization of the meta- bolic content of EVs can be further applied in EVs from ascitic fluids and peritoneal washes, which may elucidate the role of EVs as mediators of metabolic crosstalk in ovarian cancer tumour microenvi- ronment and eventually uncover potential biomarkers for ovarian cancer prognosis.