Glioblastoma Associated Natural Killer Cell EVs Generating Tumour-Specific Signatures: Noninvasive GBM Liquid Biopsy...

Diagnosis of glioblastoma (GBM) poses a recurring struggle due to many factors, including the presence of the blood-brain barrier (BBB) in addition to the significant tumor heterogeneity. Natural killer (NK) cells of the innate immune system are the primary immune surveillance mechanism for GBM and identify GBM tumors without any previous sensitization. The metabolic reprogramming of NK cells during GBM association is expected to be reflected in its extracellular vesicles. Therefore, tracking the activity of NK cell vesicles in circulation (circulating immune vesicles, CIVs) has great potential for accurate GBM diagnosis. However, identification GBM associated CIVs in circulation is immensely challenging as there is no availability of clinically validated GBM-specific circulating biomarkers. Here, we present GBM associated CIV profiling for noninvasive GBM diagnosis. We investigated the feasibility of using the signals derived from GBM associated CIVs as a de novo methodology for GBM diagnosis. An ultrasensitive sensor and a marker-free approach were essential for the detection of rare signals of GBM associated CIVs. For this purpose, we designed GBM ImmunoProfiler platform using scalable ultrafast laser multiphoton ionization mechanism and adopted surface enhanced Raman spectroscopy (SERS) ensuring simultaneous detection of multiple CIV signals to identify GBM. We experimentally demonstrated that GBM associated CIVs carry unique, tumor-specific signals. The features of GBM associated CIVs were explored through machine learning identifying its similarity with GBM patient blood (without cell isolation) using a very small amount of peripheral blood (5 μL) with 96.82% sensitivity and 100% specificity. In addition, we demonstrated that a tumor associated CIV profile can classify between multiple brain cancer types (astrocytoma, oligodendroglioma, and glioblastoma). We also experimentally demonstrated significant variation in the immune checkpoint protein expression (PDL-1 and CTLA-4) between GBM associated CIVs and uninteracted CIVs. Preclinical analysis with serum specimens of GBM patients showed the possibility of using our technology for minimally invasive GBM diagnosis. With clinical validation, our technology has potential to improve GBM diagnostics with a useful, minimally invasive GBM liquid biopsy.