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Statistical algorithm enabled high precision tumor biomarker discovery for circulating extracellular vesicle-based cancer liquid biopsy

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE246925 | GEO | 2024/01/31

REPOSITORIES: GEO

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Project description:Circulating extracellular vesicles (EVs) contain molecular footprints - lipids, proteins, metabolites, RNA, and DNA - from their cell of origin and may provide non-invasive access for detection, characterization, and monitoring of diseases. Consequently, EV-associated RNA and proteins have gained widespread interest in developing liquid-biopsy assays for cancer. Yet, an integrative proteo-transcriptomic landscape of EVs and a direct comparison with their cell of origin remains relatively unexplored. Here, we report that EVs enrich for distinct molecular cargo and their proteo-transcriptome does not linearly correlate with their cell of origin. Our sub-cellular compartment analyses revealed that EV enrich 2-6 times more cytoskeletal and extracellular proteins, while their donor cells cargo 3-10 times more nuclear and mitochondrial proteins. EVs predominantly enrich (40-60%) for small RNA between ~15-200 nucleotides, which include miRNAs, siRNAs, tRNA, 5S rRNAs, and snRNAs, associated with cell differentiation, development, and Wnt signaling signatures. Finally, our integrated proteo-transcriptomic analyses reveal that EVs are enriched with specific small RNAs (RNY3, vtRNA, and MIRLET-7) and their complementary proteins (YBX1, IGF2BP2, SRSF1/2). These analyses suggest that RNA-protein complexes may constitute a functional interaction network inside EVs to protect and regulate access to EV-RNA. To ensure that our analyses are unbiased and independent of the cell of origin, EV isolation methods, site of experimentation, site of data generation, and methodologies applied for RNAseq and mass spectrometry, we have generated and curated comprehensive datasets of proteomics and transcriptomics from a total of 12 cancer cell lines, their EVs, and prostate cancer patientsÕ serum EVs (A detailed description is provided in Fig. 1). These combined analyses demonstrated that irrespective of the cell of origin and other aforementioned conditions, EVs enrich for distinct RNA and protein signatures that do not linearly correlate with their cell of origin.

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Statistical algorithm enabled high precision tumor biomarker discovery for circulating extracellular vesicle-based cancer liquid biopsy

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