Deep sequencing of circulating exosomal microRNA allows non-invasive glioblastoma diagnosis.

Abstract

Exosomes are nano-sized extracellular vesicles released by many cells that contain molecules characteristic of their cell of origin, including microRNA. Exosomes released by glioblastoma cross the blood-brain barrier into the peripheral circulation and carry molecular cargo distinct to that of "free-circulating" miRNA. In this pilot study, serum exosomal microRNAs were isolated from glioblastoma (n = 12) patients and analyzed using unbiased deep sequencing. Results were compared to sera from age- and gender-matched healthy controls and to grade II-III (n = 10) glioma patients. Significant differentially expressed microRNAs were identified, and the predictive power of individual and subsets of microRNAs were tested using univariate and multivariate analyses. Additional sera from glioblastoma patients (n = 4) and independent sets of healthy (n = 9) and non-glioma (n = 10) controls were used to further test the specificity and predictive power of this unique exosomal microRNA signature. Twenty-six microRNAs were differentially expressed in serum exosomes from glioblastoma patients relative to healthy controls. Random forest modeling and data partitioning selected seven miRNAs (miR-182-5p, miR-328-3p, miR-339-5p, miR-340-5p, miR-485-3p, miR-486-5p, and miR-543) as the most stable for classifying glioblastoma. Strikingly, within this model, six iterations of these miRNA classifiers could distinguish glioblastoma patients from controls with perfect accuracy. The seven miRNA panel was able to correctly classify all specimens in validation cohorts (n = 23). Also identified were 23 dysregulated miRNAs in IDHMUT gliomas, a partially overlapping yet distinct signature of lower-grade glioma. Serum exosomal miRNA signatures can accurately diagnose glioblastoma preoperatively. miRNA signatures identified are distinct from previously reported "free-circulating" miRNA studies in GBM patients and appear to be superior.

• Authors: Saeideh Ebrahimkhani, Fatemeh Vafaee, Susannah Hallal, Heng Wei, Maggie Yuk T Lee, Paul E Young, Laveniya Satgunaseelan, Heidi Beadnall, Michael H Barnett, Brindha Shivalingam, Catherine M Suter, Michael E Buckland, Kimberley L Kaufman
• Journal: NPJ precision oncology (NPJ Precis Oncol) Vol. 2 Pg. 28 ( 2018) ISSN: 2397-768X [Print] England
• PMID: 30564636 (Publication Type: Journal Article)