Statistical algorithm enabled high precision tumor biomarker discovery for circulating extracellular vesicle-based cancer liquid biopsy
Circulating extracellular vesicles (EVs) have gained significant attention for discovering tumor biomarkers. However, isolating EVs with well-defined homogeneous populations from complex biological samples is challenging. Different methods have been found to derive different EV populations carrying different biomolecules, which significantly confound biomarker discovery for developing clinical diagnostics. Building a rigorous EV isolation and standardizing assessment platform associated with -omics is essential to overcome this challenge. We introduced a novel isolation approach using a pH-responsive peptide conjugated with NanoPom magnetic beads (ExCy) for homogeneous EV isolation. Additionally, we introduced the first statistical algorithm for EV quality assessment (ExoQuality Index, EQI), which enables multi-assay quantification to provide a consistent and accurate definition of EV purity and quality; ExoQuality's algorithm intakes multi-assay information to deconvolute complex EV heterogeneity. We analyzed our next generation sequencing on EV RNAs from pancreatic cancer patient plasma using four isolation methods; results highlighting ExCy's isolation and EQI assessment improved biomarker identification. We identified a novel EV tumor biomarker, ATP6V0B, validated with Quantitative PCR (qPCR) by screening a pilot cohort of 23 individuals. With random forest modeling the ATP6V0B cycling threshold, we reported an AUC of 0.91, showcasing an enabling and clinically translatable liquid biopsy approach using circulating EVs. Overall design: To benchmark our novel EV isolation method, we compared EVs isolated by different methods on the same sample sets (plasma from patients) using RNA-seq