Abstract
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.
Highlights
Pancreatic cancer is the third leading cause of cancer-related death in the United States with a median overall survival of less than one year and five-year survival of only 7.7% [1, 2]
The three main outcomes of this study were: (i) we identified panels of extracellular vesicles (EV) miRNA biomarkers based on unbiased RNA-sequencing that can distinguish healthy, precancerous lesions (PanIN), and pancreatic ductal adenocarcinoma (PDAC) mice (Fig. 1C); (ii) we used this panel of EV biomarkers in a machine-learning algorithm to predict which mice are healthy, PanIN, or PDAC, and evaluated this diagnostic in prospectively collected user-blinded samples (Fig. 1D); (iii) we determined the key signaling pathways activated in the PDAC and PanIN mice relative to the healthy mice that could be detected in the EVs, to link our biomarkers to the underlying mechanism of the disease states
Surface marker selection To enrich for pancreatic cancer cell–derived EVs, such that we could increase the performance of our machine learning–based diagnostic, we used track etched magnetic nanopore (TENPO) to evaluate the efficacy of several different combinations of previously reported surface markers to isolate tumor-derived EVs from plasma [32]
Summary
Pancreatic cancer is the third leading cause of cancer-related death in the United States with a median overall survival of less than one year and five-year survival of only 7.7% [1, 2]. The development of a diagnostic that can detect pancreatic cancer at an earlier, curative stage has been a topic of great interest [4, 7, 8], but one that has faced significant challenges [9]. EVs have three key advantages for medical diagnostics relative to circulating molecules in the blood 15–17), avoiding the counting error and need to process very large volumes of blood (V > 10 mL), which have limited CTC-based diagnostics for pancreatic cancer. The primary limitation of exosomes has been that, due to their nanoscale size, it has been challenging to apply conventional microfluidic technology to precisely sort and detect them, as has been done with great success for CTCs [18, 19]. When microfluidics are scaled to the size of exosomes (100Â smaller in dimension than CTCs), the throughput of nanoscale devices, which scales with the
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