Standardized Molecular Analyses of Glioma EVs - 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.