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Rapid Evaluation of Lung Adenocarcinoma Progression by Detecting Plasma Extracellular Vesicles with Lateral Flow Immunoassays.

Cheng LuWei XiaoYanqiong SuXuchao ZhangYixiao ChenKunjie LuPeijun TengJiajie LiangHuawen YangQifang SongYong TangDonglin Cao
Published in: ACS sensors (2023)
Extracellular vesicles (EVs) have been widely used in liquid biopsy to diagnose and monitor cancers. However, since samples containing EVs are usually body fluids with complex components, the cumbersome separation steps for EVs during detection limit the clinical application and promotion of EV detection methods. In this study, a dyad lateral flow immunoassay (LFIA) strip for EV detection, containing CD9-CD81 and EpCAM-CD81, was developed to detect universal EVs and tumor-derived EVs, respectively. The dyad LFIA strip can directly detect trace plasma samples and effectively distinguish the cancerous sample from healthy plasma. The limit of detection for detecting universal EVs was 2.4 × 10 5 mL -1 . The whole immunoassay can be performed in 15 min and only consumes 0.2 μL of plasma for one test. To improve the suitability of a dyad LFIA strip in complex scenarios, a smartphone-based photographic method was developed, which provided a consistency of 96.07% to a specialized fluorescence LFIA strip analyzer. In further clinical testing, EV-LFIA discriminated lung cancer patient groups ( n = 25) from healthy controls ( n = 22) with 100% sensitivity and 94.74% specificity at the best cutoff. The detection of EpCAM-CD81 tumor EVs (TEVs) in lung cancer plasma revealed the differences in TEVs in individuals, which reflected the different treatment effects. TEV-LFIA results were compared with CT scan findings ( n = 30). The vast majority of patients with increased TEV-LFIA detection intensity had lung masses that enlarged or remained unchanged in size, which reported no response to treatment. In other words, patients who reported no response ( n = 22) had a high TEV level compared with patients who reported a response to treatment ( n = 8). Taken together, the developed dyad LFIA strip provides a simple and rapid platform to characterize EVs to monitor lung cancer therapy outcomes.
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