Aibaidula, Abudumijiti

Abstract INTRODUCTION Extracellular Vesicles (EVs) are lipid bilayer particles present in almost all biofluids and carry tumor-specific molecular cargo. Plasma EVs are promising sources of biomarker discovery for glioblastoma (GBM). The clinical relevance of non-neoplastic plasma EVs in GBM remains unclear. In this study, we determine if associations exist between plasma EV subpopulation frequencies in GBM and clinical factors. METHODS Platelet-free plasma samples of 63 GBM, 43 Grade 2 glioma, and 45 healthy donors were included. All glioma plasma samples were collected before surgery. Plasma EV subpopulation frequency based on surface expression of EV-associated tetraspanins (CD9/CD81/CD63) and markers of cell origin (CD45/CD41a/CD11b/CD31) was determined by spectral flow cytometry. Clinical information including age, surgery, MGMT methylation status, corticosteroid usage, tumor volume, and overall survival data was recorded. RESULTS Compared to normal donors, GBM patients had increased CD9+, CD63+, CD81+, and CD11b+ EVs but decreased CD41a+ EVs. GBM patients also had the highest frequency of myeloid-derived CD9+CD11b+ EVs (20.17%) followed by grade 2 gliomas (13.78%) and normal donors (2.76%, p< 0.001). More GBM patients (29/63, 46.0%) received preoperative corticosteroids than grade 2 glioma patients (9/43, 20.9%; p = 0.008). Corticosteroid use was not associated with differences in EV subpopulation frequencies in GBM or grade 2 glioma patients. Pearson correlation analysis showed weak but statistically significant correlations between GBM total tumor volume and CD9+ EVs (p = 0.014), CD41a+ EVs (p = 0.037), and CD9+/CD63-/CD81- EVs (p = 0.007). In GBM patients, younger age (< 60 yrs, p = 0.01), gross total resection (p = 0.02), MGMT methylation (p = 0.002), high CD9+ EV expression (p = 0.011), low CD9+CD63-CD81- EV expression (p = 0.009) and low CD9+CD11b+ EV expression (p = 0.006) correlated with longer overall survival. CONCLUSION Differentially expressed non-neoplastic plasma EV subpopulations have significant clinical relevance in GBM including association with tumor volume and predicting survival. Validation of those markers in larger cohorts could reveal new prognostic biomarkers for GBM.

Abstract INTRODUCTION: Plasma extracellular vesicles (EVs) have been shown as a promising source for biomarker identification in glioblastoma (GBM) and could help differential diagnosis, treatment evaluation and tumor progression monitoring. These EVs are enriched in molecular signatures indicative of their cell origins, giving an indication of the key players in this pathology. In this project, we aimed to identify diagnostic biomarkers for GBM plasma EVs and their cells of origin. METHODS: Plasma EV samples were prepared following the MIFlowCyt-EV guideline of the International Society for Extracellular Vesicles, then stained for EV markers (CD9/CD63/CD81) and markers indicative of cell origins (CD31/CD45/CD41a/CD11b). Actin phalloidin was used as a negative marker. Stained plasma samples were analyzed using a Cytek Aurora flow cytometer. Percentages of different EV subpopulations were analyzed and compared between GBM and normal donor (ND) plasma EVs (reference group). Further clustering analysis was performed on EV events by t-distributed stochastic neighbor embedding (t-SNE) and self-organizing maps on flow cytometry data (FlowSOM) analysis. The predictive value of multiparametric qualities derived from the reference group was tested in blinded test group samples. RESULTS: Percentages of CD9, CD81, and CD11b positive EVs were higher in GBM patient plasma, while ND plasma had more CD41a positive EVs. GBM plasma EVs had unique multiparametric signatures compared to ND plasma EVs based on t-SNE and FlowSOM analysis. Our analysis also identified 15 distinct EV subpopulations which differed in size and various surface marker expression levels. Eight of these subpopulations were enriched for GBM EVs, while three were enriched for ND EVs. Our method of multiparametric analysis demonstrates high sensitivity and specificity in predicting disease status in human samples. CONCLUSIONS: GBM plasma EVs have a unique surface marker expression profile and distinct EV subpopulations compared to ND plasma EVs. Multiparametric signatures show promise as potential diagnostic markers of GBM.

2038 Background: Glioblastoma (GBM) is the most common malignant brain tumor with poor clinical prognosis. Management of GBM is hampered by the lack of an accurate test that can be used for differential diagnosis of tumor progression from inflammatory pseudoprogression. Plasma extracellular vesicles (EVs) has been shown to be a promising source for biomarker identification. In this project, we aimed to identify GBM plasma EV markers that could serve as the basis of a liquid biopsy. Methods: Sample preparation, assay controls and instrument calibration were performed following MIFlowCyt-EV guideline. Plasma samples were subjected to 2-step centrifugations to remove cell debris and platelets. 10ul of plasma sample was diluted with 90ul filtered PBS, then stained for EV surface markers including CD9, CD31, CD45, CD41a and CD11b, as well as actin phalloidin. Stained plasma samples were purified using IZON qEV1/70nm column, then EV fractions were analyzed using full spectrum Cytek Aurora flow cytometer. Clustering analysis was performed on EV events (CD9 +/ actin phalloidin -) using t-SNE and FlowSOM extensions from FlowJo plugins. Results: Compared to normal donors, GBM plasma EVs were bigger in size (higher SSC value) and expressed higher levels of CD9, CD31, CD45 and CD11b while ND plasma EVs had higher CD41a expression. t-SNE and FlowSOM analysis demonstrated that GBM plasma EVs had a unique surface marker expression profile compared to ND EVs. It also showed 10 EV sub-populations that differed in size as well as various surface marker expression levels. Four of these subpopulations were enriched in GBM EVs, while three of these were enriched in ND EVs. Conclusions: This multiparametric analysis revealed that GBM plasma EVs had a unique surface marker expression profile compared to ND plasma EVs. Further separation and molecular profiling analysis based on each sub population could reveal EV biomarkers that are unique to each sample population.