Abstract
Glioblastoma cells secrete extra-cellular vesicles (EVs) containing microRNAs (miRNAs). Analysis of these EV miRNAs in the bio-fluids of afflicted patients represents a potential platform for biomarker development. However, the analytic algorithm for quantitative assessment of EV miRNA remains under-developed. Here, we demonstrate that the reference transcripts commonly used for quantitative PCR (including GAPDH, 18S rRNA, and hsa-miR-103) were unreliable for assessing EV miRNA. In this context, we quantitated EV miRNA in absolute terms and normalized this value to the input EV number. Using this method, we examined the abundance of miR-21, a highly over-expressed miRNA in glioblastomas, in EVs. In a panel of glioblastoma cell lines, the cellular levels of miR-21 correlated with EV miR-21 levels (p<0.05), suggesting that glioblastoma cells actively secrete EVs containing miR-21. Consistent with this hypothesis, the CSF EV miR-21 levels of glioblastoma patients (n=13) were, on average, ten-fold higher than levels in EVs isolated from the CSF of non-oncologic patients (n=13, p<0.001). Notably, none of the glioblastoma CSF harbored EV miR-21 level below 0.25 copies per EV in this cohort. Using this cut-off value, we were able to prospectively distinguish CSF derived from glioblastoma and non-oncologic patients in an independent cohort of twenty-nine patients (Sensitivity=87%; Specificity=93%; AUC=0.91, p<0.01). Our results suggest that CSF EV miRNA analysis of miR-21 may serve as a platform for glioblastoma biomarker development.
Highlights
Glioblastoma is the most common form of primary brain cancer and remains one of the deadliest of human cancers [1]
We first determined the relative abundance of GAPDH mRNA, 18S rRNA, and hsa-miR-103 in extra-cellular vesicles (EVs) derived from 6 glioblastoma and 2 non-glioblastoma adherent cell lines
Analysis of genetic material within glioblastoma secreted within EVs in bio-fluids represents a unique opportunity for diagnosis and therapeutic monitoring
Summary
Glioblastoma is the most common form of primary brain cancer and remains one of the deadliest of human cancers [1]. Diagnosis and sensitive therapeutic monitoring remain major challenges in the treatment of this disease. Response evaluations are largely based on clinical examination and Magnetic Resonance Imaging (MRI) [2]. Both clinical examination and MRI are insensitive measures of disease status. The lowest resolution for reliable detection by MRI is on the order of millimeters [3]. While repeated post-treatment biopsies constitute a monitoring option, this practice is associated with significant morbidity [5,6]. In this context, less invasive platforms for therapeutic monitoring are needed
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