Zhao Chen, Wang Zhili, Kim Hyoyong, Kong Hui, Lee Junseok, Yang Jacqueline Ziqian, Wang Anmin, Zhang Ryan Y, Ju Yong, Kim Jina, Feng Bing, Liu Dejun, Zhang Yating, Wang Zhenfang, Zhang Yandong, Guo Shujing, Gao Dekang, Tomlinson James S, Pei Renjun, Wan Jipeng, Pandol Stephen J, Sim Myung-Shin, You Sungyong, Ma Ding, Lu Shaohua, Sun Na, Tseng Hsian-Rong, Zhu Yazhen
Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles (UCLA), Los Angeles, CA, 90095, USA.
California NanoSystems Institute, Crump Institute for Molecular Imaging, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles (UCLA), Los Angeles, CA, 90095, USA.
Adv Sci (Weinh). 2025 Jun;12(21):e2414982. doi: 10.1002/advs.202414982. Epub 2025 Mar 25.
Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related mortality, largely due to late-stage diagnosis. Reliable early detection methods are critically needed. PDAC-derived extracellular vesicles (EVs) carry molecules that reflect their parental tumor cells and are detectable in early disease stages, offering a promising noninvasive diagnostic approach. Here, a streamlined PDAC EV Surface Protein Assay for quantifying PDAC EV subpopulations in 300-µL plasma through a two-step workflow is presented: i) click chemistry-mediated EV enrichment using EV Click Beads and trans-cyclooctene-grafted antibodies targeting three PDAC EV-specific surface proteins (MUC1, EGFR, and TROP2), and ii) quantification of enriched PDAC EVs through reverse transcription-quantitative polymerase chain reaction. The three PDAC EV-specific surface proteins are identified using a bioinformatics framework and validated on PDAC cell lines and tissue microarrays. The resultant PDAC EV Score, derived from signals of the three PDAC EV subpopulations, demonstrates robust differentiation of PDAC patients from noncancer controls, with area under the receiver operating characteristic curves of 0.94 in the training (n = 124) and 0.93 in the validation (n = 136) cohorts. This EV-based diagnostic approach successfully exploits PDAC EV subpopulations as novel biomarkers for PDAC early detection, translating PDAC surface proteins into an EV-based liquid biopsy platform.
胰腺导管腺癌（PDAC）是癌症相关死亡的主要原因，很大程度上是由于晚期诊断。迫切需要可靠的早期检测方法。源自PDAC的细胞外囊泡（EVs）携带反映其亲代肿瘤细胞的分子，并且在疾病早期阶段可检测到，提供了一种有前景的非侵入性诊断方法。在此，提出了一种简化的PDAC EV表面蛋白检测方法，通过两步工作流程对300μL血浆中的PDAC EV亚群进行定量：i）使用EV Click Beads和靶向三种PDAC EV特异性表面蛋白（MUC1、EGFR和TROP2）的反式环辛烯接枝抗体进行点击化学介导的EV富集，以及ii）通过逆转录-定量聚合酶链反应对富集的PDAC EV进行定量。使用生物信息学框架鉴定三种PDAC EV特异性表面蛋白，并在PDAC细胞系和组织微阵列上进行验证。从三个PDAC EV亚群的信号得出的最终PDAC EV评分显示，PDAC患者与非癌症对照有明显区分，在训练队列（n = 124）中的受试者操作特征曲线下面积为0.94，在验证队列（n = 136）中为0.93。这种基于EV的诊断方法成功地将PDAC EV亚群用作PDAC早期检测的新型生物标志物，将PDAC表面蛋白转化为基于EV的液体活检平台。
