Extracellular vesicles (EVs) have emerged as a promising surrogate for tissue biopsy, potentially enabling non-invasive, real-time cancer monitoring. Most cancer cells release large numbers of EVs into circulation that carry molecular constituents reflective of the heterogeneity of the parent tumor. This project is designed to optimize a liquid biopsy to diagnose malignant glioma tumors. Currently, such tumors are diagnosed through a brain tissue biopsy which involves considerable risk for patients and doesn’t allow for longitudinal follow up of clinical care. Current EV isolation and characterization methods yield inconsistent results and render data reproducibility challenging, often leading to unpredictable conclusions. The goals of this project are to i) address variability among the different EV isolation methods and platforms currently available, and to ii) pinpoint to the “best” method to validate candidate biomarkers for glioma diagnosis. Our exceptional investigative team brings together experts in malignant glioma treatment, the field of nano-engineering, vesicular research, assay development and droplet digital PCR technology to optimize the necessary elements for the development of a blood-based assay capable of moving towards clinical settings. Through a simple blood test, clinicians will be able to diagnose, stratify and monitor a tumor without the need for tissue biopsy. Our strategic partnership with Exosome Diagnostics, an industry leader in EV-based cancer diagnostics, offers us venues allowing for the translation of our findings, coupled with access to clinical grade kits, platforms and study design. The D epartment of Neurosurgery and the Center for Systems Biology at the Massachusetts General Hospital comprise multidisciplinary clinical expertise, innovative technologies and complementary resources to carry out the following translational projects: First, based on our prior kit comparison work, we have picked two top EV isolation kits and enrichment platforms to test in a series of well controlled, reference standards to determine an optimal EV isolation method. Second, we will test whether EV gene signatures can be used as biomarkers for cancer detection as well as tracking recurrence. By following quality control on device design and sample processing, accruing well-annotated patient and control samples, and performing multi site testing, we will ensure assay reliability and reproducibility to deliver clinically translatable EV diagnostics. Fourteen genes were selected through literature data mining based on the putative evidence that they can distinguish gliomas from controls. Finally, a gene’s signature with the highest sensitivity and specificity will be validated in a large cohort of patient samples. The technical and scientific outcomes of this research could have a significant translational impact in gliomas, establishing a robust, highly specific assay to guide treatment decision and assess tumor recurrence.