Background
Breast cancer is the most commonly diagnosed cancer in women worldwide. The identification of breast cancer molecular biomarkers would provide a more accurate assessment of individual disease risks and prognosis. Exosomes, small extracellular vesicles, have been shown to contribute to various aspects of cancer development and progression. Within the last decade, the content of exosomes has been increasingly explored as a new source of potential biomarker molecules for early disease detection.
Methods
Exosomal proteomes of MDA-MB-231, a metastatic breast cancer cell line, and MCF-10A, a non-cancerous epithelial breast cell line, were compared. Proteomic analysis was conducted using nano-liquid chromatography coupled to tandem mass spectrometry. The expression of proteins in MDA-MB-231cells was analyzed using label-free protein quantification methods. For the selection of potential biomarkers, the following criteria were used: (i) proteins must be unique to MDA-MB-231 cells when compared to MCF-10A cells, ii) localized on the membrane, (iii) abundant in breast cancer and (iii) are reported to increase in expression as the disease progresses. The presence of selected proteins on exosomes was verified using flow cytometry methods.
Results
In total, 1,107 exosomal proteins were identified in both cell lines, 726 of which were unique to the MDA-MB-231 breast cancer cell line. The biomarker selection process identified three exosomal proteins (glucose transporter 1, glypican 1, and “disintegrin and metalloproteinase domain-containing protein 10”) as potential breast cancer biomarkers. The presence of these three
proteins was validated using flow cytometry methods. The proteomics dataset was also rich in other interesting breast cancer proteins, such as 16 metastasis-associated proteins and two kinases.
Conclusion
We demonstrate that breast cancer exosomes are a rich source of protein biomarkers that may be beneficial for diagnosis and prognosis.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/40444 |
| Date | 01 May 2020 |
| Creators | Risha, Yousef |
| Contributors | Berezovski, Maxim |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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