BIOM-66. MULTIPARAMETRIC QUALITIES OF PLASMA EXTRACELLULAR VESICLES AS NOVEL DIAGNOSTIC BIOMARKERS FOR GLIOBLASTOMA. (14th November 2022)

• Record Type: Journal Article
• Authors: Aibaidula, Abudumijiti(Zack); Fain, Cori; Garcia, Luz Cumba; Jung, Miyeon; Johnson, Aaron; Parney, Ian
• Is Part Of: Neuro-oncology. Volume 24(2022)Supplement 7
• Page Start: vii19
• Page End: vii20
• Publication Date: 2022-11-14
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noac209.076
• Languages: English
• ISSNs: 1522-8517

Abstract

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.