= Emerging. More than 5 years before clinical availability. (24.37%, 2023)
= Expected to be clinically available in 1 to 4 years. (39.50%, 2023)
= Clinically available now. (36.13%, 2023)
MSACL 2023 : Dorado

MSACL 2023 Abstract

Self-Classified Topic Area(s): Lipidomics

Podium Presentation in Steinbeck 2 on Wednesday at 14:00 (Chair: Anne Bendt / Frederick Strathmann)

Extracellular Vesicles Are a Source of Lipid Biomarkers for Breast and Ovarian Cancer Diagnosis

Erika Dorado (1), M Luisa Doria (1), James S McKenzie (1), Stefania Maneta-Stavrakaki (1), Anika Nagelkerke (2), Jeremy Nicholson (1), R Charles Coombes (3), Molly M Stevens (2), Zoltan Takats (1)
(1) Faculty of Medicine, Division of Systems Medicine, Imperial College London, London, United Kingdom; (2) Faculty of Engineering, Department of Materials, Imperial College London, United Kingdom, (3) Faculty of Medicine, Department of surgery and Cancer, Imperial College London, United Kingdom

Erika Dorado, MSc, PhD (Presenter)
Imperial College London

Presenter Bio: Erika Dorado is a postdoctoral researcher at Imperial College London working on lipidomic analysis and the identification of body-fluid based biomarkers for cancer diagnosis. She completed her PhD in Clinical Medicine Research at Imperial College London working on the lipidomic analysis of extracellular vesicles and its potential for cancer diagnosis. Before starting her PhD, she worked on proteomics and phosphoproteomics analysis in cancer signaling pathways at the Barts Cancer Institute. Her MSc was focused on -omics technologies, where she worked on analysis of protein-protein interactions using cross-linking mass spectrometry. She obtained her BSc in Biology focused in molecular and cell biology, and after that worked in the study of mutations in antimalarial and malaria diagnosis targets. She is one of the leads of the MSACL Early Career Network (MSACL ECN).


Cancer cells produce extracellular vesicles (EVs) which can be found in cancer patients’ body fluids, making these nanovesicles a promising target for liquid biopsies. Breast cancer is the most common cancer among women, and ovarian cancer the most lethal gynaecological cancer. Identification of body fluid-based biomarkers could contribute to a much earlier diagnosis of both breast and ovarian cancer, and improve patient survival rates.

The main objective was to study whether there is potential in the EVs’ lipid composition for the identification of biomarkers for breast and ovarian cancer diagnosis.

Untargeted lipidomic analyses of cancerous and non-cancerous mammary and ovarian epithelial cells and their secreted EVs, as well as EVs found in human blood plasma, were carried out. The cell panel of cancerous mammary cells studied included nine breast cancer cell lines representative of the ER+/PR+, HER2+ and triple negative breast cancer subtypes. The ovarian epithelial cells included two high-grade serous and two endometrioid ovarian cancer cell lines. The EVs produced in vitro by epithelial cells were isolated and purified by ultrafiltration and size-exclusion chromatography (SEC). The EVs found in blood plasma were isolated by a bespoke method based on the combination of density gradient ultracentrifugation and SEC for lipidomic analysis. The blood plasma was collected from women with breast cancer (n=19, 10 patients had primary breast cancer) and healthy women (n=10) who participated voluntarily in the study. The untargeted lipidomic analysis was carried out by reversed-phase liquid chromatography mass spectrometry. The data acquisition was performed in both positive and negative electrospray ionisation modes. Univariate and multivariate analyses were carried out, including logistic regression classification with recursive feature elimination (LR-RFE) analysis.

Breast and ovarian cancer-derived EVs were enriched in sphingolipids (Cer, SM and HexCer) and glycerophospholipids (LPC, LPE, PC, PE, PI, PA, ether PE and ether PC) when compared to their parental cells, which were mainly enriched in triglycerides. We found that EVs and their parental cells can be perfectly classified (100% accuracy) into their respective breast cancer subtype (ER+/PR+, HER2+ and triple negative breast cancer subtypes) based on their lipid composition. We also found that the high-grade serous and the endometrioid ovarian cancer cells and their secreted EVs can also be classified with 100% accuracy. Our in vitro study also showed that EVs and their parental cells can be classified into cancerous or non-cancerous based on specific lipid species, this applied for both breast and ovarian cancers. Equally, our analysis of EVs found in blood plasma showed that specific PE and PC species allowed us to distinguish between EVs isolated from blood plasma from breast cancer patients (n=19, 90% accuracy) and healthy volunteers (n=10, 100% accuracy). The same result was obtained for the analysis of EVs found in blood plasma from patients with primary breast cancer (n=10, 90% accuracy), which demonstrates the relevance of these findings in the early detection of breast cancer.

EVs are an important source of lipid biomarkers for EV-based liquid biopsy for early detection of cancer.

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