Tumor-derived extracellular vesicles for cancer disease management
Due to the COVID-19 crisis measurements the PhD defence of Afroditi Nanou will take place online without the presence of an audience.
The recording of this defence will be added to the video overview of recent defences.
Afroditi Nanou is a PhD student in the research group Medical Cell Biophysics (MCBP). Her supervisor is prof.dr. L.W.M.M. Terstappen from the Faculty of Science and Technology.
After a general introduction in Chapter 1, we showed in Chapters 2 and 3, that large tdEVs in blood of metastatic prostate, breast, colorectal and non-small cell lung cancer patients have equivalent prognostic power as CTCs. Importantly, patients with metastatic prostate, breast and colorectal cancer with favorable CTC counts could be further stratified using tdEV counts implying that a subset of patients with favorable CTCs and unfavorable tdEV have a relatively poor outcome and may benefit from a more aggressive treatment similarly to the patients with unfavorable CTC counts.
In Chapter 4, we identified large leukocyte-derived extracellular vesicles (ldEVs) in blood of healthy individuals and metastatic cancer patients after immunomagnetic EpCAM enrichment and fluorescent labelling with the CellSearch system. Whereas tdEVs were 20-fold more frequent as compared to CTCs in metastatic cancer patients, the frequency of ldEVs were 5-fold less frequent as compared to leukocytes coming along with the enrichment in both patients and controls. Fluorescence microscopy imaging of whole blood showed the presence of ldEVs in a 3-fold lower frequency as compared to leukocytes suggesting that the “fragmentation” of leukocytes into ldEVs is not caused by processing of blood samples in the CellSearch system and thus, are actually present in blood.
In Chapter 5, protocols were developed to image CTCs and tdEVs of castration-resistant prostate cancer patients isolated by the CellSearch system and CTCs isolated using 5 μm filters by scanning electron microscopy (SEM). SEM images of CellSearch enriched CTCs and tdEVs were obtained, but detailed morphologic information was obscured by the presence of ferrofluid, whereas in case of filtration the cells were clearly deformed by the pressure the cells undergo, while entering the filter holes. Interestingly, using SEM many microparticles were observed with similar morphology and size as large tdEVs (EpCAM+, CK+, CD45-, DAPI-) but not detected by fluorescence microscopy. Whether they originate from the tumor or not remains to be further investigated.
In Chapter 6, the addition of the HER2 antibody in the CellSearch assay revealed the presence of HER2+, CK-, CD45- CTCs and tdEVs in the blood of breast cancer patients and had a similar association with poor clinical outcome as the CK+, CD45- CTCs. The larger frequency of tdEVs allowed the assessment of the presence of HER2 in a larger portion of patients and encourage the examination of more treatment targets on tdEVs. Importantly, these results pave the path towards a more rational and objective choice of patients who will or will not be subjected to HER2 targeting therapies.
In Chapter 7, we compared the presence of CTCs and tdEVs before initiation of therapy and after the first cycle of therapy in CRPC, mBC and mCRC patients to evaluate the effect of therapy on CTCs versus tdEVs. The association between CTCs and tdEVs with overall survival was similar before the initiation of therapy but after the first cycle of therapy, CTCs outperformed tdEVs in mCRC implying that tdEV secretion is dependent on the treatment and possibly the cancer type. The distinction of different tdEV classes using a deep learning approach encourages us to determine the ones that rise or decline after the administration of an effective therapy in order to improve the evaluation of therapy responses and the patient treatment monitoring.
In Chapter 8, we investigated whereas we can detect endothelium-derived EVs (edEVs) in the CellSearch image datasets acquired from the CD146 enriched blood samples of metastatic colorectal cancer (mCRC) patients. Circulating endothelial cells (CECs) are significantly elevated in the blood circulation of cancer patients compared to healthy individuals; their presence is however not associated with better or poorer clinical outcome. The CEC number is biased by venipuncture procedure as endothelial cells are released due to the vacuum and enter the collection tube. edEVs should not be influenced; so we explored their presence through ACCEPT analysis and revealed that edEVs are detected at 5- to 10- fold higher frequencies compared to CECs. Moreover, their counts correlated with the clinical outcome of the patients. Importantly, the final multivariate Cox regression model included both tdEVs and edEVs as significant independent prognostic markers of the overall survival of mCRC patients. The elevated edEV counts denote either their active role in promoting tumor angiogenesis or/and their passive secretion because of the growth of the tumor. If the former hypothesis is correct, then edEVs could serve as a promising biomarker to predict patients that could benefit from anti-angiogenic treatments. Whether edEVs could serve as a more informative diagnostic tool in cardiovascular diseases remains to be investigated.
In Chapter 9, three different (des)biotin liposomes were compared in terms of their fusion with cells from cancer cell lines and blood from healthy individuals. DOPC liposomes of similar size distribution containing 20 mol% DOPE-desbiotin, 20 mol% DOPE-biotin or 5 mol% chol-EG3-biotin were prepared by extrusion. Their mean hydrodynamic diameter was around 100 nm. Leukocyte subpopulations, platelets and different cancer cell lines were incubated with the different liposome systems, stained with fluorophore-tagged streptavidin and their fusion with liposomes was assessed by flow cytometry and immunofluorescence microscopy. The chol-EG3-biotin liposomes achieved the highest biotin incorporation into the cell membrane for all different cell types, followed by DOPE-biotin liposomes. DOPE-desbiotin liposomes did not show considerable fusion with any cell type. The highest liposome uptake was found in cancer cells followed by the monocytes indicating a relationship between available cell surface and liposome uptake.
