| Autor: |
David Issadore, Ben Z. Stanger, Erica L. Carpenter, Junhyong Kim, Stephen Fisher, Young-Ji Na, Stephanie S. Yee, Taylor Black, Neha Bhagwat, Jina Ko |
| Rok vydání: |
2023 |
| DOI: |
10.1158/0008-5472.c.6510323 |
| Popis: |
Improved diagnostics for pancreatic ductal adenocarcinoma (PDAC) to detect the disease at earlier, curative stages and to guide treatments is crucial to progress against this disease. The development of a liquid biopsy for PDAC has proven challenging due to the sparsity and variable phenotypic expression of circulating biomarkers. Here we report methods we developed for isolating specific subsets of extracellular vesicles (EV) from plasma using a novel magnetic nanopore capture technique. In addition, we present a workflow for identifying EV miRNA biomarkers using RNA sequencing and machine-learning algorithms, which we used in combination to classify distinct cancer states. Applying this approach to a mouse model of PDAC, we identified a biomarker panel of 11 EV miRNAs that could distinguish mice with PDAC from either healthy mice or those with precancerous lesions in a training set of n = 27 mice and a user-blinded validation set of n = 57 mice (88% accuracy in a three-way classification). These results provide strong proof-of-concept support for the feasibility of using EV miRNA profiling and machine learning for liquid biopsy.Significance: These findings present a panel of extracellular vesicle miRNA blood-based biomarkers that can detect pancreatic cancer at a precancerous stage in a transgenic mouse model. Cancer Res; 78(13); 3688–97. ©2018 AACR. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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