PATH-10. UTILIZATION OF TUMOR-DERIVED EXTRACELLULAR VESICLES FOR PATIENT STRATIFICATION AND BIOMARKER DISCOVERY

Abstract

Abstract Liquid biopsy is poised to play an increasingly important role in clinical decision making by enabling earlier cancer detection, diagnosis, patient stratification, and treatment monitoring. Most approaches rely on detection of cell-free circulating tumor DNA or circulating tumor cells in the peripheral blood of cancer patients. However, there are several limitations to these approaches, including miniscule abundance, unfavorable signal to noise ratios, and limited insight into mechanisms driving disease. Extracellular vesicles (EVs) are an alternative liquid biopsy analyte comprising lipid membrane encapsulated particles carrying diverse protein, nucleic acid, and metabolite cargos. Blood plasma of cancer patients contains EVs derived directly from tumor tissue that serve as rich sources of insight into tumor biology and disease pathology. We have developed a clinically applicable, multi-omic EV subpopulation interrogation pipeline that robustly profiles tumor-derived EVs in biofluids. We have demonstrated the ability of the platform to enrich cancer markers and detect activation of pathways driving oncogenesis across several different cancers, including brain cancer. There is a particularly compelling need for liquid biopsy-based solutions in neuro-oncology. We have applied our EV-omic (EVO) pipeline in combination with machine learning to distinguish adult high-grade gliomas from benign brain tumor and healthy patients, using blood-based EV proteomic and transcriptomic signatures. Marker profiles found to be most important for patient stratification combine markers known to be relevant in tumor tissue along with novel features. Furthermore, IDH status and MGMT methylation metadata derived from matched patient tissue for select cases was used to generate unique blood-based profiles for glioma subtypes. Lastly, we show preliminary longitudinal data demonstrating the utility of EVs to track tumor changes post-surgical resection and post-treatment. This work demonstrates that EV-omics can add significant value in the neuro-oncology space.