恶性肿瘤
仿形(计算机编程)
医学
胰腺癌
癌症研究
病理
计算生物学
内科学
生物
癌症
计算机科学
操作系统
作者
Katherine S. Yang,Hyungsoon Im,Seonki Hong,Ilaria Pergolini,Andres Fernandez del Castillo,Rui Wang,Susan M. Clardy,Chen-Han Huang,Craig Pille,Soldano Ferrone,Robert Yang,Cesar M. Castro,Hakho Lee,Carlos Fernández del Castillo,Ralph Weissleder
出处
期刊:Science Translational Medicine
[American Association for the Advancement of Science (AAAS)]
日期:2017-05-24
卷期号:9 (391)
被引量:226
标识
DOI:10.1126/scitranslmed.aal3226
摘要
Pancreatic ductal adenocarcinoma (PDAC) is usually detected late in the disease process. Clinical workup through imaging and tissue biopsies is often complex and expensive due to a paucity of reliable biomarkers. We used an advanced multiplexed plasmonic assay to analyze circulating tumor-derived extracellular vesicles (tEVs) in more than 100 clinical populations. Using EV-based protein marker profiling, we identified a signature of five markers (PDACEV signature) for PDAC detection. In our prospective cohort, the accuracy for the PDACEV signature was 84% [95% confidence interval (CI), 69 to 93%] but only 63 to 72% for single-marker screening. One of the best markers, GPC1 alone, had a sensitivity of 82% (CI, 60 to 95%) and a specificity of 52% (CI, 30 to 74%), whereas the PDACEV signature showed a sensitivity of 86% (CI, 65 to 97%) and a specificity of 81% (CI, 58 to 95%). The PDACEV signature of tEVs offered higher sensitivity, specificity, and accuracy than the existing serum marker (CA 19-9) or single-tEV marker analyses. This approach should improve the diagnosis of pancreatic cancer.
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