Circulating EVs long RNA-based subtyping and deconvolution enable prediction of immunogenic signatures and clinical outcome for PDAC.

Identification of clinically applicable molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) is crucial to improving patient outcomes. However, the traditional tissue-dependent transcriptional subtyping strategies are invasive and not amenable to routine clinical evaluation. In this study, we developed a circulating extracellular vesicle (cEV) long RNA (exLR)-based PDAC subtyping method and provided exLR-derived signatures for predicting immunogenic features and clinical outcomes in PDAC. We enrolled 426 individuals, among which 227 PDACs served as an internal cohort, 118 PDACs from two other medical centers served as an independent validation cohort, and 81 healthy individuals served as the control. ExLR sequencing was performed on all plasma samples. We found that PDAC could be categorized into three subtypes based on plasma exLR profiles. Each subpopulation showed its own molecular features and was associated with patient clinical prognosis. The immunocyte-derived cEV fractions were altered among PDAC subtypes and interconnected with tumor-infiltrating lymphocytes in cancerous tissue. Additionally, we found a significant concordance of immunoregulators between tissue and blood EVs, and we harvested potential PDAC therapeutic targets. Most importantly, we constructed a nine exLR-derived, tissue-applicable signature for prognostic assessment of PDAC. The circulating exLR-based features may offer an attractive platform for personalized treatment and predicting patient outcomes in multiple types of cancer.

EV-origin: Enumerating the tissue-cellular origin of circulating extracellular vesicles using exLR profile.

Extracellular vesicles (EVs) are complex ecosystems that can be derived from all body cells and circulated in the body fluids. Characterizing the tissue-cellular source contributing to circulating EVs provides biological information about the cell or tissue of origin and their functional states. However, the relative proportion of tissue-cellular origin of circulating EVs in body fluid has not been thoroughly characterized. Here, we developed an approach for digital EVs quantification, called EV-origin, that enables enumerating of EVs tissue-cellular source contribution from plasma extracellular vesicles long RNA sequencing profiles. EV-origin was constructed by the input matrix of gene expression signatures and robust deconvolution algorithm, collectively used to separate the relative proportions of each tissue or cell type of interest. EV-origin respectively predicted the relative enrichment of seven types of hemopoietic cells and sixteen solid tissue subsets from exLR-seq profile. Using the EV-origin approach, we depicted an integrated landscape of the traceability system of plasma EVs for healthy individuals. We also compared the heterogenous tissue-cellular source components from plasma EVs samples with diverse disease status. Notably, the aberrant liver fraction could reflect the development and progression of hepatic disease. The liver fraction could also serve as a diagnostic indicator and effectively separate HCC patients from normal individuals. The EV-origin provides an approach to decipher the complex heterogeneity of tissue-cellular origin in circulating EVs. Our approach could inform the development of exLR-based applications for liquid biopsy.