DEVELOPING AN EXTRACELLULAR VESICLE-BASED MULTI-OMIC LIQUID BIOPSY BIOMARKER SIGNATURE OF GLIOMA USING SAMPLES FROM THE GENOMICS ENGLAND BIOBANK

Abstract AIMS Glioma diagnosis requires an invasive tissue biopsy that limits longitudinal sampling. This results in a more limited understanding of the changes that occur in response to treatment and over time compared to other solid tumours. Extracellular vesicles (EVs) have been proposed as a potential liquid biopsy biomarker as their cargo has been demonstrated to reflect their cell of origin. Through the integration of plasma derived-EVs proteomics, transcriptomics, and Raman spectroscopy data we aim to develop a glioma biomarker signature. METHODS Using 1mL plasma from glioma patients (n=57) in the Genomics England biobank, and sex- and age-matched volunteers (n=49), EVs were separated by size exclusion chromatography (qEV1/35nm) and characterised as per MISEV2023 (protein quantification, nanoparticle tracking analysis, transmission electron microscopy and Western blot). Statistically significant differences in EV characteristics were assessed using an unpaired Welch’s t-test. Subsequently, analysis of the multi-omic cargo and label-free Raman spectral signature of the EVs were investigated and machine-learning approaches were used to identify a glioma biomarker signature. RESULTS Following EV separation there was no difference in the mean particle yield between glioma patients and healthy volunteers. However, there was a significant difference in the mean protein yield (p=0.0104), and the mean (p<0.0001) and median (p<0.0001) particle size. Further, there was a significant increase in the concentration of particles >80nm (p=0.0024). Multi-omic analysis of ~1000 identified EV proteins and EV micro-RNA cargo plus Raman spectra identification revealed significant differences between glioma and healthy volunteers’ EV samples. Analysis, incorporating machine-learning algorithms, is ongoing to integrate the identified multi-omic signatures into a comprehensive glioma biomarker, and investigate a signature to differentiate glioma subtypes. CONCLUSION Multi-omic analysis of plasma derived-EVs look promising for developing a glioma liquid biopsy signature. Fur- ther analysis is ongoing to define an integrated biomarker signature and will be presented.