The World Health Organization counts cancer as the major, ever increasing cause of deaths worldwide. A striking example is breast cancer, which is the main cause of cancer death of Swiss women. The essential problem of this cancer is that it only becomes symptomatic at an advanced stage of the tumor, highlighting the importance of the established systematic screening campaigns. In most developed countries, these campaigns encourage every woman over 50 to undergo a mammography every two years, consisting in a radiological examination of the breasts using X-rays. Unfortunately, mammography screening suffers from sensitivity, specificity and dependency on invasive biopsies.
To address these issues, a team of scientists at the Ecole Polytechnique Fédérale de Lausanne (EPFL), in close collaboration with medical experts from the Centre Hospitalier Universitaire Vaudois (CHUV), is developing a unique blood test for early diagnosis of breast cancer. The proposed biosensor aims to leverage the power of extracellular vesicles (EVs) as an exceptional reservoir of tumor biomarkers, based on their isolation and analysis from microliters of whole blood. EVs are highly promising diagnostic candidates as they are secreted by cancer cells into their microenvironment and ultimately into blood. These nanometer-sized containers are cell-derived vesicles enclosed by a lipid bilayer, which transport the molecular identity of their mother cells and therefore indicate the type and state of progression of the tumor. The isolation and detection of these nanovesicles, which appear in blood at early cancer stages, will enable a remote liquid biopsy as well as a less invasive and continuous access to breast tumor states, therefore offering a novel strategy in cancer diagnosis and personalized medicine without injuring the patients. The use of this technology could become a standard for breast cancer diagnosis at a very early stage and pave the way for new systematic screening campaigns of other proliferative diseases.
Was ist das Besondere an diesem Projekt?
The innovativeness of our project resides in the ability to extract biochemical information of EVs in a label-free mode using a surface-sensitive biosensor. The information, stored in the form of a spectral fingerprint, enables the differentiation of EVs originating from healthy donors and cancer patients. The biosensor will deliver unprecedented real-time information on the state of progression of the cancer, resulting in an innovative remote and painless biopsy of the tumor mass undetectable with classical methods.
We have analyzed with our sensing unit EVs isolated from different cultured cell lines of human breast cancer representing different classes of breast carcinoma. Their molecular compositions and structural features lead to unique spectral fingerprints, tightly associated with their cellular origins. Hence, the analysis of a large number of EVs allowed the generation of a predictive model based on multivariate analysis, which cluster and classify these cell lines according to distinct spectral characteristics. Using this method, samples of unknown origin were identified with a reliability of more than 91%. This innovative methodology is currently the subject of a patent application. Following these results, we will start the feasibility assessment with clinical samples in collaboration with Prof. Delaloye, head of Breast unit, Dr. Isabelle Guilleret of the Clinical Research Support Platform and Dr. Laurence Chapatte of the Biobanque Institutionnelle de Lausanne of the CHUV.
Wyss R., Grasso L., Wolf C., Grosse W., Demurtas D. & Vogel H. Molecular and dimensional profiling of highly purified extracellular vesicles by fluorescence fluctuation spectroscopy. Anal. Chem. 86, 7229–7233 (2014);
Grasso, L., Wyss, R., Weidenauer, L., Thampi, A., Demurtas, D., Prudent, M., et al. Molecular screening of cancer-derived exosomes by surface plasmon resonance spectroscopy. Anal. Bioanal. Chem., 407, 5425–5432 (2015).
Am Projekt beteiligte Personen
Letzte Aktualisierung dieser Projektdarstellung 17.10.2018