Purpose: To determine whether a multi-analyte liquid biopsy can improve the detection and staging of pancreatic adenocarcinoma (PDAC).

be reflective of their cells of origin. For a wide range of clinical questions across diseases including cancer, infectious diseases, and traumatic brain injury, EVs have demonstrated unique clinical potential as a blood-based source of biomarkers that can be used as an adjunct or alternative to standard-of-care tissue biopsy. [1][2][3][4][5][6][7] Unlike liquid biopsy analytes such as circulating tumor cells (CTCs), EVs are abundant in circulation (1-10 CTCs mL −1 in cancer patients [8] vs up to 10 12 EVs mL −1 in serum [9] ); however, precise numbers for the concentration of most disease-associated EVs in patient blood have not been established. Moreover, single EVs contain multiple proteins and nucleic acid cargoes from their cells of origin, [10] yielding a more comprehensive view of the complex, heterogeneous, and often dynamically changing disease states, especially when compared to single-analyte readouts such as circulating serum-based antigens (e.g., carcinoembryonic antigen, CEA). [11] Also, EVs are a growing area of biological inquiry, in particular for their role in intercellular interactions, including interactions with immune cells [12] and metastatic sites in cancer. [13] Despite their widely appreciated potential as biomarkers, EVs have yet to be translated to widespread clinical use beyond proofof-concept studies. One fundamental hurdle to the translation of EV biomarkers to address clinical questions is the lack of an adequately specific, robust, and reproducible isolation technology for relatively sparse disease-associated EVs from the large background of EVs present in the blood. [2,6,10] To solve this problem, we were inspired by the field of CTC isolation, where rare cells (as rare as 1 in 10 9 hematologic cells in patient blood) tagged with antibodyfunctionalized magnetic nanoparticles (MNPs) have been precisely isolated from or quantified in whole blood by matching the feature size of the sorting or detection element on microfluidic chips to the given target analyte (e.g., 9-19 µm for a CTC). [14][15][16] The nanoscale size (30-200 nm for exosomes, [17] 50 nm − 1 µm for microvesicles [5] ) of EVs, however, has made it challenging to develop technology analogous to what has been successful in CTCs due to the difficulty and expense of fabricating nanoscale devices and their susceptibility to clogging. As a result, many Extracellular vesicles (EVs) -nanoscale membranous particles that carry multiple proteins and nucleic acid cargoes from their mother cells of origin into circulation -have enormous potential as biomarkers. However, devices appropriately scaled to the nanoscale to match the size of EVs (30-200 nm) have orders of magnitude too low throughput to process clinical samples (10 12 EVs mL −1 in serum). To address this challenge, we develop a novel approach that incorporates billions of nanomagnetic sorters that act in parallel to precisely isolate sparse EVs based on immunomagnetic labeling directly from clinical samples at flow rates billions of times greater than that of a single na...

Liquid biopsy is the analysis of materials shed by tumors into circulation, such as circulating tumor cells, nucleic acids, and extracellular vesicles (EVs), for the diagnosis and management of cancer. These assays have rapidly evolved with recent FDA approvals of single biomarkers in patients with advanced metastatic disease. However, they have lacked sensitivity or specificity as a diagnostic in early-stage cancer, primarily due to low concentrations in circulating plasma. EVs, membrane-enclosed nanoscale vesicles shed by tumor and other cells into circulation, are a promising liquid biopsy analyte owing to their protein and nucleic acid cargoes carried from their mother cells, their surface proteins specific to their cells of origin, and their higher concentrations over other noninvasive biomarkers across disease stages. Recently, the combination of EVs with non-EV biomarkers has driven improvements in sensitivity and accuracy; this has been fueled by the use of machine learning (ML) to algorithmically identify and combine multiple biomarkers into a composite biomarker for clinical prediction. This review presents an analysis of EV isolation methods, surveys approaches for and issues with using ML in multianalyte EV datasets, and describes best practices for bringing multianalyte liquid biopsy to clinical implementation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The isolation of specific subpopulations of extracellular vesicles (EVs) based on their expression of surface markers poses a significant challenge due to their nanoscale size (< 800 nm), their heterogeneous surface marker expression, and the vast number of background EVs present in clinical specimens (1010-1012 EVs/mL in blood). Highly parallelized nanomagnetic sorting using track etched magnetic nanopore (TENPO) chips has achieved precise immunospecific sorting with high throughput and resilience to clogging. However, there has not yet been a systematic study of the design parameters that control the trade-offs in throughput, target EV recovery, and specificity in this approach. We combine finite-element simulation and experimental characterization of TENPO chips to elucidate design rules to isolate EV subpopulations from blood. We demonstrate the utility of this approach by increasing specificity > 10x relative to prior published designs without sacrificing recovery of the target EVs by selecting pore diameter, number of membranes placed in series, and flow rate. We compare TENPO-isolated EVs to those of gold-standard methods of EV isolation and demonstrate its utility for wide application and modularity by targeting subpopulations of EVs from multiple models of disease including lung cancer, pancreatic cancer, and liver cancer.

The isolation of specific subpopulations of extracellular vesicles (EVs) based on their expression of surface markers poses a significant challenge due to their nanoscale size (< 800 nm), their heterogeneous surface marker expression, and the vast number of background EVs present in clinical specimens (1010–1012 EVs/mL in blood). Highly parallelized nanomagnetic sorting using track etched magnetic nanopore (TENPO) chips has achieved precise immunospecific sorting with high throughput and resilience to clogging. However, there has not yet been a systematic study of the design parameters that control the trade-offs in throughput, target EV recovery, and ability to discard background EVs in this approach. We combine finite-element simulation and experimental characterization of TENPO chips to elucidate design rules to isolate EV subpopulations from blood. We demonstrate the utility of this approach by reducing device background > 10× relative to prior published designs without sacrificing recovery of the target EVs by selecting pore diameter, number of membranes placed in series, and flow rate. We compare TENPO-isolated EVs to those of gold-standard methods of EV isolation and demonstrate its utility for wide application and modularity by targeting subpopulations of EVs from multiple models of disease including lung cancer, pancreatic cancer, and liver cancer.