Combining Machine Learning and Nanofluidic Technology To

Oct 11, 2017 - (28, 42-50) Specifically, we first generated a list of genes known to be differentially expressed in pancreatic cancer cells(28, 42-50)...
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Jina Ko,† Neha Bhagwat,‡ Stephanie S. Yee,§ Natalia Ortiz,† Amine Sahmoud,‡ Taylor Black,‡ Nicole M. Aiello,‡ Lydie McKenzie,§ Mark O’Hara,§ Colleen Redlinger,⊥ Janae Romeo,⊥ Erica L. Carpenter,§ Ben Z. Stanger,‡ and David Issadore*,†,∥ †

Department of Bioengineering and ∥Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States ‡ Division of Gastroenterology and §Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine and ⊥ Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States S Supporting Information *

ABSTRACT: Circulating exosomes contain a wealth of proteomic and genetic information, presenting an enormous opportunity in cancer diagnostics. While microfluidic approaches have been used to successfully isolate cells from complex samples, scaling these approaches for exosome isolation has been limited by the low throughput and susceptibility to clogging of nanofluidics. Moreover, the analysis of exosomal biomarkers is confounded by substantial heterogeneity between patients and within a tumor itself. To address these challenges, we developed a multichannel nanofluidic system to analyze crude clinical samples. Using this platform, we isolated exosomes from healthy and diseased murine and clinical cohorts, profiled the RNA cargo inside of these exosomes, and applied a machine learning algorithm to generate predictive panels that could identify samples derived from heterogeneous cancer-bearing individuals. Using this approach, we classified cancer and precancer mice from healthy controls, as well as pancreatic cancer patients from healthy controls, in blinded studies. KEYWORDS: cancer diagnostics, exosomes, nanofluidics, machine learning, pancreatic cancer rare cells,6,14−18 the scaling of these approaches to the nanoscale has been limited by the inherently low-throughput and susceptibility to clogging of nanoscale fluid channels. To address these challenges, we have developed an approach for exosome isolation, wherein millions of nanofluidic exosome sorting components are incorporated onto a single chip and work in parallel to isolate exosomes from clinical samples. Our exosome track-etched magnetic nanopore (ExoTENPO) chip rotates conventional nanofluidic sorting by 90° to form magnetic traps at the edges of pores instead of in channels. By distributing the flow over millions of nanoscale pores, (1) flow rates (Φ > 10 mL/h) 106× greater than in individual nanofluidic devices can be achieved while preserving the precision of nanofluidic immunomagnetic sorting and (2) sorting can be performed directly on unprocessed serum or

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any tumors (e.g., from the brain, pancreas, and lung) are located in regions of the body that are difficult to surgically access, and for which repeat biopsy is often impossible.1−5 Liquid biopsy, the minimally invasive detection of tumor material in patient blood samples, has generated many opportunities for the diagnosis and monitoring of cancer.6,7 Nanoscale exosomes (30−200 nm diameter) released during fusion of the multivesicular endosomes (MVEs) with the plasma membrane, and which are found circulating in the blood, have been discovered to contain molecular information on their cells of origin, relevant for disease diagnostics, disease monitoring, and drug efficacy screening.8−12 It has proven challenging to use conventional technology to establish the clinical utility of exosomal biomarkers and to utilize these biomarkers to improve patient care. Due to exosomes’ small size, conventional size-based isolation is time-consuming (>6 h), results in copurification of cell debris, and cannot select specific subpopulations of exosomes or differentiate exosomes from other extracellular vesicles (e.g., microvesicles, oncosomes).7,13 While microfluidic technology can sort and detect © 2017 American Chemical Society

Received: August 2, 2017 Accepted: October 11, 2017 Published: October 11, 2017 11182

DOI: 10.1021/acsnano.7b05503 ACS Nano 2017, 11, 11182−11193

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Figure 1. ExoTENPO-based exosome capture. (a) A photograph of the ExoTENPO and the NdFeB external magnet with an SEM image of the magnetic nanopores; scale bar: 600 nm. (b) A schematic of our chip-based assay. From plasma, magnetically labeled exosomes are isolated, their mRNAs are isolated and profiled using qPCR, and the signature is found using a machine learning algorithm. (c) Finite element simulations of ExoTENPO. The field strength |B| is plotted on the cross-section of a 600 nm pore. An exosome passing through the pore experiences both a drag force Fd from the fluid flow and a magnetophoretic force Fm toward the pore’s edge where the magnetic field gradient is maximized. (d) SEM image of exosomes captured at the edge of the ExoTENPO pores. The small round objects are unbound magnetic nanoparticles (MNPs). (e) The size distribution of exosomes isolated using ExoTENPO measured by DLS. The input, cell culture media, consisted of primarily debris (d = 10.1 nm), whereas the exosomes isolated on ExoTENPO consisted primarily of exosomes (a = 149.7 ± 11.5 nm). (N = 3 technical replicates, ± represents standard deviation.) (f) The relative RNA expression level of 12 genes were compared between ExoTENPO and centrifugation method. The PCR threshold cycles (Ct) are plotted versus one another and showed positive correlation (R2 = 0.8). (g) The relative expression levels of RNA cargos from cells and exosomes are quantified from five different human pancreatic cancer cell lines, which showed positive correlation between cells and exosomes (h). (R2 = 0.88, N = 3 technical replicates, N = 2 biological replicates.)

plasma without risk of clogging, as the occlusion of any single nanopore results in diversion of the flow to a nearby pore. In addition to the challenge of isolating tumor-derived exosomes, the successful diagnosis of cancer is made more challenging due to the complex nature of the disease and its heterogeneity both within the tumor itself and between patients.40 Conventional methods that rely on only a single molecular biomarker51,52 are often not sufficiently specific. Any single protein or nucleic acid biomarker can be controlled by many complex processes and will not necessarily map directly to a specific disease state that is universally true for all patients. As an alternative, a panel of molecular biomarkers can be measured to more comprehensively profile the complex state of the cancer. However, it can be difficult to make sense of multiple molecular biomarkers. To solve this challenge, we use machine learning algorithms and training sets of data to extract a set of optimized linear discriminators from a panel of RNA biomarkers. These discriminators are then evaluated using

independent blinded test sets of data that are separate from the training data. By choosing relevant states of the cancer (precancerous lesions, cancer, healthy) and training the machine learning algorithm to find patterns that optimally classify these states, we outperform any individual marker in blinded studies. We validated the utility of our ExoTENPO chip for cancer diagnostics by focusing on pancreatic cancer, in particular on the currently intractable problem of detecting the disease at an early stage.19,20 Using this approach, in blinded test sets, we were able to correctly classify healthy subjects from those with pancreatic cancer, in both a murine model and in a clinical cohort. Moreover, in the murine model, pancreatic intraepithelial neoplasia (PanIN) mice with premalignant pancreatic lesions were correctly identified relative to the healthy mice and mice with cancer. 11183

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(2) Weak drag forces (FD↓) at high volumetric flow rates (Φ↑). To isolate an exosome, the magnetic trapping force Fm must overcome the drag force Fd. The drag force is proportional to the flow velocity of the fluid Fd ∝ v. The ExoTENPO chip consists of an Adev = 15.2 cm2 sized membrane densely covered (ρ > 107/cm2) with magnetic nanopores. Thus, even at the high volumetric flow rates (Φ > 10 mL/h), the flow velocity v, and thus the drag force Fd, within each pore vz = Φ/(AdevρApore) can be kept small, where Apore is the cross sectional area of an individual pore. Finite Element Simulation. We have constructed a finite element model to simulate the magnetic trapping capability of the ExoTENPO (Figure 1c). The field strength B drops rapidly in distance from the edge of the nanopore, creating field gradients ∇B that lead to strong magnetic forces Fm. To be trapped, an exosome must first be translated radially by magnetophoretic forces Fr to the trap at the edge of the pore. The radial force Fr drops off quickly in distance from the pore’s edge (Figure S1), motivating the design goal of minimizing the pore diameter d to bring the exosome into close proximity to the strongest field regions. Once translated to the pore’s edge, the magnetophoretic trapping force Fz > 0.2 nN must overcome the drag force Fd to successfully trap the exosome (Figure S1). In this chip, the magnetic trapping force far exceeds the drag force Fz ≫ Fd at the pore’s edge, for flow rates as large as Φ = 100 mL/h. From this analysis, we extract three useful insights: (1) The capture rate decreases as flow rate Φ increases; (2) the capture rate increases as the pore’s diameter d decreases; and (3) because the probability of capturing an exosome is a function of its initial radial position in the pore, the capture rate can be increased by placing multiple ExoTENPO membranes in series (Figure S2), allowing multiple, independent chances for capture. Multiple layers of ExoTENPO membranes are incorporated into a microfluidic device using laser cut laminate microfluidics (Figure S3). Characterization of ExoTENPO’s Performance. We characterized the ExoTENPO’s capture efficiency using 50 nm MNPs (Miltenyi) that have a uniform diameter and magnetization. To determine the concentration of nanoparticles, we measured the nuclear magnetic resonance T2 relaxation time (Bruker mq60 MR relaxometer) operating at 1.41T (Figure S4a), making use of the fact that the concentration of MNPs CMNP is proportional to 1/T2 ∝ CMNP.23 As expected, the capture rate of MNPs decreased as flow rate increased (Figure S4b). To calculate capture rate, we first generated a calibration curve by measuring the NMR relaxation time T2 for various concentrations of MNPs CMNP. The capture rate ζ is defined as ζ = Cinput/Coutput, where Cinput is the concentration of MNPs before sorting and Coutput is the concentration of MNPs in the flow-through of the device. After we measured the T2 relaxation time for the input and output suspensions, we used the calibration curve to calculate the concentration of particles in the input Cinput and in the flowthrough Coutput and reported the capture rate ζ. The capture rate ζ for a double filter (n = 2) decreased as a function of the flow rate Φ as a power law, ζ = 264.6Φm , m = −0.87 (R2 = 0.88). As predicted, the capture rate ζ could be recovered at high flow rates Φ by stacking multiple layers n of the ExoTENPO membranes (Figure S4c). The capture rate ζ was found to increase exponentially with the number n where ζ = 9.1e0.64n (R2 = 0.92). This exponential increase (Figure S4c)

RESULTS/DISCUSSION The Exosome Track-Etched Magnetic Nanopore (ExoTENPO) Chip. The ExoTENPO isolates specific subtypes of exosomes by immunomagnetically labeling exosomal protein surface markers and subsequently capturing these targeted exosomes directly from unprocessed serum or plasma (Figure 1a). Both the paramagnetic material in ExoTENPO and the superparamagnetic magnetic nanoparticles (MNPs) that label the exosomes are magnetized by an external NdFeB disc magnet (d = 1 1/2 in., h = 3/4 in., K&J Magnetics) placed immediately below the ExoTENPO device. The exosomes captured on the ExoTENPO are subsequently lysed on-chip, and their mRNA cargo is extracted and analyzed by quantitative PCR (qPCR). The qPCR data are analyzed using a machine learning algorithm to predict the state of the subject (i.e., Healthy, PanIN, or Cancer) (Figure 1b). The ExoTENPO consists of a track-etched polycarbonate membrane coated with a 200 nm layer of magnetic material (Ni80Fe20, permalloy). The ExoTENPO device has an Adev = 15.2 cm2 sized membrane densely covered (ρ > 107/cm2) with magnetic nanopores (600 nm diameter) (Figure 1a). The nanopores are designed to be big enough to not trap particles that are known to be present in serum or plasma (e.g., nucleic acid aggregates, cell debris, lipoproteins).62,63 Exosomes are magnetically labeled using two-step immunomagnetic labeling, with biotinylated antibodies and streptavidin-coated 50 nm MNPs (Miltenyi), allowing 10× less antibody usage than direct nanoparticle conjugation and more particle loading.24 Exosomes are labeled by targeting either tetraspanins (CD9, CD81), known to be expressed by exosomes, or markers that will enrich for exosomes derived from tumors of epithelial origin (e.g., EpCAM). Compared to other immunolabeling technologies where exosomes are directly captured to microbeads or on antibody-coated surfaces, here exosomes are labeled with multiple MNPs, and the ExoTENPO selectively captures exosomes that have been labeled with sufficient MNPs to overcome the drag force. Therefore, background material with few nonspecifically bound MNPs can be discarded by using a sufficiently high flow rate. The key to ExoTENPO’s ultrasensitive isolation of exosomes is its magnetic nanopore design, which maximizes the magnetophoretic force Fm that isolates nanoscale exosomes while minimizing the competing drag force Fd. The two innovations that allow ExoTENPO to combine the precision of nanofluidic immunomagnetic sorting with a throughput sufficient for clinical diagnostics are detailed below: (1) Strong magnetophoretic forces (Fm↑). The magnetophoretic force Fm ∼ (B·∇)B on targeted exosomes is maximized by increasing the strength of the applied field B, increasing its spatial changes ∇B, and by delivering the targeted object as close to the magnetic trap as possible. Large fields (0.4 T) are produced by an external NdFeB magnet. Large field gradients ∇B are created by the NiFe-coated nanopores, which generate nanoscale field gradients at the pore’s edge, where there is a transition from the highly magnetically susceptible NiFe (χ ≅ 105) to water (χ ≅ −10−5). The nanoscale traps can be created because there is no inherent length-scale in Maxwell’s equations for magnetostatics, in contrast to optical trapping where the size of a trap is limited by the wavelength of light.21 11184

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markers (Alix, TSG101) and negative for Calnexin, known to not be expressed by exosomes.60 In comparison, isolates captured using ultracentrifugation were not only positive for Alix and TSG101 but also positive for Calnexin, suggesting increased copurification of nonexosomal material. We processed the material missed by our device, its flow through, using ultracentrifugation and found it to be positive for exosome markers and also positive for Calnexin, suggesting that our device did miss some exosomal material and that our device successfully discarded nonexosomal material positive for Calnexin. Additionally, we measured the platelet marker CD61, to check for background from the abundant platelet derived exosomes,61 and found that it was negative in the isolate of our device, but positive in the isolate of ultracentrifugation (Figure S7b). Isolation of RNA Cargo from Exosomes. We extracted RNA from exosomes directly on the ExoTENPO membrane for downstream analysis. Total exosomal RNA and protein isolation kit (Life Technologies) was used for RNA extraction from isolated exosomes. Briefly, first denaturing solution was added to the chip, and the chip was incubated for 5 min on ice. Then, the lysed solution was taken off-chip for acid-phenol separation and washing steps using a spin column. The exosomal RNA was eluted in a small volume (∼30 μL) and stored at −80 °C or processed immediately for further analysis. We first compared our chip’s extraction of RNA to the yield of a conventional ultracentrifugation and a commercial kit (Total Exosome Isolation Kit, Life Technologies), which is known to result in higher yield than conventional ultracentrifugation.54 We used both cell culture media and a KPCY (Kras, p53, Pdx1-Cre, Yfp) mouse model, which is genetically engineered to develop pancreatic cancer. Using human pancreatic cancer cell cultured media, we achieved a 4−6× improvement in the quantity of RNA recovered compared to both ultracentrifugation and the commercial kit (Figure S7c). We also measured the size of particles isolated by ultracentrifugation where we observed a broader peak (119 nm ±113 nm) (Figure S7d) compared to that of particles isolated by ExoTENPO (149.7 nm ±100 nm) (Figure 1e). The size and quality of the exosomal RNA extracted from mice plasma were characterized using a Bioanalyzer. Exosomal RNA isolated using both the centrifugal method and our chip had the greatest occurrence at 24 bp, with a range from 22 to 30 bp for exosomes from a KPCY mouse (Figure S8a,b) and 25−200 bp for exosomes from a cell culture media. (Figure S9a,b). Using human pancreatic cancer cell cultured media and panexosome markers, we validated that the relative quantities of exosomal mRNA measured in exosomes isolated using the ExoTENPO matched that of the isolate acquired using a centrifugal method. We quantified 12 different genes using qPCR and measured their mRNA expression levels and found a positive correlation (R2 = 0.74) between the relative abundance of RNA isolated using ExoTENPO and a centrifugal method (Figure 1f). Thus, the ExoTENPO chip enables the efficient isolation of exosomes, whose RNA profile closely resembles that of the conventionally isolated material. We chose the genes in the panel because they are known to be differentially expressed in pancreatic cancer cells relative to the healthy state.28,42−50 Specifically, we first generated a list of genes known to be differentially expressed in pancreatic cancer cells28,42−50 and then, using the online database exocarta.org, selected a subset that were known to be packaged in exosomes.

arises from the fact that each layer of ExoTENPO contains pores that have a random position, uncorrelated with the pores in the other layers. As an exosome passes through the multiple ExoTENPO membranes, separated by a 100 μm gap, it will pass through each pore at a random initial position and therefore have an independent probability of being captured in each layer; the probability of an exosome being captured by multiple ExoTENPOs in series compounds with the passage through each subsequent ExoTENPO (Figure S2). By placing n = 6 ExoTENPO in series, a high enrichment ζ > 500 was achieved at a flow rate sufficient for running clinical samples Φ = 10 mL/h. We verified that as the pore size was decreased from d = 3 μm to d = 600 nm, and there was a significant increase in the capture rate ζ (Figure S4d). The above experiment was performed using a double (n = 2) filter at Φ = 1 mL/h. To validate that the ExoTENPO isolated exosomes primarily due to magnetic trapping, we performed a negative control experiment where we turned off the magnetophoretic force by removing the external NdFeB magnet, which resulted in no particles captured ζ = 1 (Cinput = Coutput). Characterization and Validation of Exosome Isolation Using ExoTENPO. To validate that our ExoTENPO can capture exosomes, we first isolated exosomes derived from cell culture media. Testing was carried out at Φ = 10 mL/h and with 6 layers of ExoTENPO membranes using human and mouse cell culture media as a source of exosomes (MiaPaCa2, Panc-1, AsPC1, BxPC3, PD7591) and performing isolation using either pan-exosome (CD9, CD63, CD81) or epithelial markers (EpCAM). We performed the following characterization experiments to validate that our isolate contained material consistent with the standards laid out by the International Society for Extracellular Vesicles.59 First, we fixed the exosomes directly on the ExoTENPO nanopores after capture using pan-exosome markers and imaged them using scanning electron microscopy (SEM) (Figures 1d and S5) and observed objects with a morphology consistent with exosomes covered with 50 nm magnetic nanoparticles. We also fixed the exosomes eluted from the ExoTENPO device and compared them to the exosomes isolated using ultracentrifugation (Figure S6). Both methods are enriched for objects that have morphology and sizes that match with those of exosomes. However, we were able to observe more material in the isolate of ultracentrifugation that had a size larger than that consistent with exosomes d > 200 nm. We also analyzed the input and the isolate using dynamic light scattering (DLS). In the input, there was a distinct distribution of particles at d = 50.7 nm and a larger population of smaller particles (a = 10.1 nm) that are likely debris (Figure 1e). We eluted the exosomes captured by the ExoTENPO and found using DLS that 90% of the particles had a diameter a = 149.7 nm ±100 nm, consistent with that of exosomes (Figure 1e). We quantified the specificity of ExoTENPO capture by comparing the quantity of exosomal RNA capture from cell culture media using the ExoTENPO, using pan-exosome markers (CD9, CD63, CD81) versus a control wherein the antibodies were replaced with an isotype control (biotin mouse IgG1 k isotype, Biolegend). There was >10× RNA yield (P < 0.05) using the pan-exosome markers as affinity ligands compared to the isotype control, confirming the specificity of the ExoTENPO (Figure S7a). We additionally performed a Western blot on the isolate of our device, using EpCAM based capture on 500 μL healthy mouse plasma spiked with 15 mL PD7591 mouse pancreatic cancer cell cultured media, and showed that the isolate was positive for exosome 11185

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Figure 2. Pancreatic cancer diagnosis for mice using a machine learning based mRNA signature. (a) The KPCY pancreatic cancer mouse model includes a fluorescent (YFP) lineage tracer to stage the mice. (b) Pipeline of our machine learning based diagnosis. Training set is fed into LDA to generate LDA vectors, which are applied to the blinded test set for classification (c). After threshold detection, predicted labels are compared to the true labels to evaluate the performance (d). (e) Using the training set, we generated LDA scores (wT · x) for individual mice. LDA scores classify mice into the correct categories. (f) N-1 cross validation was performed on the training set to evaluate the performance, where all mice were classified into the right groups. (g) In an independent blinded test set, all mice were classified correctly. (h) The performance of our training set (AUC = 1) was compared to a scrambled training set (AUC = 0.53).

This list was validated in the subsequent in vitro and in vivo experiments, resulting in a panel of eight exosomal mRNAs. We compared the mRNA expression levels of exosomes isolated using our chip with those of their cells of origin and found a positive correlation. We measured the mRNA expression using qPCR in both the cellular lysate and in the exosome isolate for five different human pancreatic cancer cell lines and six different highly expressed mRNAs from our list (Figure 1g). The mRNA expression in this analysis was normalized to the mean expression for each sample. The mRNA expression levels from exosomes and cells were positively correlated (R2 = 0.88) (Figure 1h).25 Diagnosis of Pancreatic Cancer Using Machine Learning Based mRNA Signatures. To evaluate the performance of the ExoTENPO for the detection of pancreatic cancer, we first conducted a study on a cohort of KPCY mice, which are genetically engineered to develop pancreatic cancer (Figure 2a).27 The pancreatic cells in the KPCY mice express yellow fluorescent protein (YFP), enabling the true disease state to be known. Thus, these mice are well suited to evaluate our chip’s capability to detect disease at different stages. The exosomal mRNA signatures of mice that are healthy, mice with precancerous lesions (PanIN), and mice with tumors were measured, and from these measurements, a predictive panel of exosome-based biomarkers for pancreatic cancer was developed and tested using an independent, user blinded cohort of mice (N = 18). We selected a panel of candidate exosomal mRNA biomarkers using qPCR that are known to be differentially expressed in pancreatic cancer relative to healthy state.28,42−50 We isolated exosomes from approximately 0.5 mL

of undiluted plasma from each mouse on the ExoTENPO (n = 6 membranes) based on their expression of epithelial cell adhesion molecule (EpCAM), which is known to be expressed by cancer cells of epithelial origin. We measured the exosomal mRNA profile using qPCR from a training set of N = 5 healthy, N = 5 PanIN, and N = 5 mice with tumors. Among these mice, by measuring only the total quantity of RNA, it was not possible to accurately classify the mice (P > 0.5) (Figure S10a). Among the panel of mRNA that we measured, several genes were differentially expressed between the groups (e.g., H3F3A) (Figure S10b). However, no single gene was able to classify individual mice into the correct groups, due to the variance in expression among mice within groups. We used linear discriminant analysis (LDA) to identify linear combinations of the mRNA profile that can discriminate between healthy mice, mice with PanINs, and mice with tumors. We first generated a prediction algorithm by running LDA on a training set wT, where the true state of each subject is a priori known T (Figure 2b). The LDA algorithm generates a vector x that is used to calculate a weighted sum w × x that maximally separates the healthy controls from either the subjects with PanIN lesions or with tumors. These LDA vectors were initially tested on the training set by performing N-1 cross validation29 and were then subsequently evaluated using a blinded test set wB (Figure 2c), independent from the training set, where the true state of the mouse was not known a priori (Figure 2d). We used this technique to first do a pairwise differentiation between subjects that are healthy and those with either PanINs or tumors (Figure 2e). In both cases, there exists a threshold value ψ, above which subjects are predicted to be 11186

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Figure 3. Exosomes captured using a specific surface marker (EpCAM). (a) The exosomal mRNA expression level difference between healthy plasma and spiked plasma (mouse cancer cell cultured media spiked into healthy mouse plasma) was measured from pan exosome (Pan) based (CD9, CD81) isolation and EpCAM-based isolation, where EpCAM-based isolation showed higher difference (N = 3 technical replicates, N = 3 biological replicates). (b) EpCAM+ exosomes captured using ExoTENPO were measured using DLS for size distribution. The peak is at 169.9 nm. (c) Comparison of diagnostic performance between EpCAM capture and Pan capture for heathy versus tumor mice. (d) Comparison of diagnostic performance between EpCAM capture and Pan capture for heathy versus PanIN mice.

Ct,healthy, where Ct,spiked is the qPCR threshold cycle measured in the isolate from the spiked sample and Ct,healthy is the threshold cycle from the isolate of the healthy control. By using DLS, we confirmed that EpCAM captured exosomes had an average diameter a = 169.9 nm (Figure 3b), consistent with the results found using the pan-exosome marker (Figure 1e). To test the value of our ExoTENPO’s surface marker specific isolation of exosomes, we compared our device’s ability, using the same mRNA markers and the same LDA approach described in Figure 2, to diagnose pancreatic cancer using EpCAM-based isolation versus a pan-exosome (CD9, CD81) isolation in a blinded test set (Figure 3c,d). We showed that using EpCAM isolation, all mice could be correctly discriminated, whereas using pan-exosome (CD9, CD81) isolation, which isolates all exosomes in circulation, the performance was markedly degraded (AUC = 0.81 for tumor versus healthy and AUC = 0.53 for PanIN versus healthy) (Figure 3c,d). Clinical Diagnosis of Pancreatic Cancer with ExoTENPO. To evaluate the ExoTENPO’s capability to diagnose pancreatic cancer in clinical specimens, we first characterized the performance of the chip isolating exosomes from human blood samples. We compared the recovery of exosomal RNA from healthy human plasma samples using the ExoTENPO (using pan-exosome isolation: CD63, CD9, CD81) to a centrifugal method (Total Exosome Isolation Kit, Life Technologies) and found a 1.6× improvement in recovery (Figure S11). We compared the recovery of exosomal RNA in a variety of common clinical sample types, including fresh

one class and below which they are predicted to be in the other class. N-1 cross validation on these pairwise comparisons results in correct identification of each mouse (Figure 2f). Subsequently, in an independent user-blinded test set, which was separate from the training set, all mice (N = 18) were correctly identified as either tumor, PanIN, or healthy (Figure 2g). We also validated the specificity of the predictive power of LDA analysis by comparing the performance to a data set where a training set of scrambled group labels was created. As expected, the prediction algorithm generated by the scrambled training set had no predictive value, resulting in an AUC of 0.5 and 0.53 for healthy versus PanIN and healthy versus tumor, respectively (Figure 2h). Enrichment of Specific Exosome Populations from Complex Media. One key advantage of the ExoTENPO is its capability to specifically capture subpopulations of exosomes based on the expression of particular surface markers. Beyond its use in diagnostics, this capability can be used to aid in the study of the biological function of subtypes of exosomes.26 To demonstrate this capability, we showed that subtypes of exosomes could be isolated directly from plasma. We spiked pancreatic cancer cell cultured media (PD6910) into healthy mouse plasma to compare exosomal mRNA isolated using a cocktail of pan-exosome markers (CD9, CD81) to exosomes isolated using EpCAM. Compared to pan-exosome marker based capture, EpCAM based isolation was able to more specifically isolate a subset of epithelial cell-derived exosomes from the background of healthy plasma (Figure 3a). We quantified this specificity by calculating ΔCt = Ct,spiked − 11187

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ACS Nano plasma, fresh serum, frozen plasma, and frozen serum using pan-exosome isolation and found no significant difference in the RNA recovery from the various sample types (P = 0.23) (Figure S12a). Next, we performed pairwise comparisons of the relative abundance of mRNA targets (CK18, CD63, Erbb3, KRAS) and found that similar exosomal RNA cargo profiles (R2 > 0.77) were obtained from the different sample preparations (Figure S12b). Thus, our platform can be used on any of these available sample types and provide comparable information. To identify the affinity ligand to use for our clinical measurements, we compared using pan-exosome isolation versus cancer epitopes to enrich for subset of tumor-derived exosomes from plasma. We spiked human pancreatic cancer cell culture media into healthy human plasma to compare isolated exosomes using a cocktail of pan-exosome markers (CD63, CD9, CD81) to those using a candidate tumor marker, EpCAM. Compared to pan-exosome marker-based capture, positive capture using EpCAM showed the greatest mRNA expression level difference ΔCt = Ct,spiked − Ct,healthy between cancer exosome spiked plasma Ct,spiked and healthy plasma Ct,healthy (Figure S12c). We conducted a study on an independent blinded test set of N = 34 clinical samples, including previously untreated metastatic pancreatic cancer patients and healthy controls (Figure S13). The same panel of eight exosomal mRNA biomarkers identified in our mouse studies was used. For each subject, we isolated exosomes from 3 mL of undiluted plasma using EpCAM-based isolation on the ExoTENPO. We first measured the exosomal mRNA profile from a training set of N = 5 healthy controls and N = 5 patients with cancer (Figure 4a). Among the panel of mRNA that we measured, several genes were differentially expressed between the groups (e.g., CD63), but no single gene was able to classify individual patients into the correct groups due to the variance in expression among patients within groups. We first evaluated the diagnostic ability of this approach using N-1 cross-validation to prevent overfitting29 and classified every patient into the correct group (Figure 4b). To validate this approach, we applied this technique to our independent, user-blinded test set (N = 24) where we classified each patient correctly, demonstrating a positive predictive value (P < 0.001, Fisher’s Exact Test) (Figure 4c). Additionally, we directly compared the performance of LDA of our panel of eight exosomal RNA biomarkers to that of fewer biomarkers, down to only a single biomarker (Figure 4d). As the number of genes increased, our diagnostic was able to detect the mRNA signature associated with specific disease states, and the performance quickly increased, achieving correct classification for N ≥ 4 genes in a blinded test set.

Figure 4. Diagnosis of human pancreatic cancer using a machine learning based mRNA signature. (a) LDA scores were algorithmically generated based on the RNA expression levels to separate healthy (green) from cancer patients (red). (b) The capability to discriminate healthy versus cancer was first tested using N-1 crossvalidation of the training set and was able to correctly discriminate every patient, summarized in a confusion matrix. (c) An independent, user blinded test set was created and was able to discriminate all patients with cancer vs. healthy donors, summarized in a confusion matrix. (d) ROC curves were generated based on varying numbers of exosomal mRNA. The inset shows that AUC is a function of the number of exosomal RNA biomarkers. The color coding matches the points in the inset to the ROC curves.

of continuous variables. More complicated machine learning algorithms can be used to further improve performance39 but were not explored in this study due to the limited number of samples N. This work builds on the success of using microfluidic immunomagnetic sorting to isolate rare cells, achieving selectivity and sensitivity not possible using macroscale technologies.17,30−32,64 This work also builds on previous work by our group using a technique to sort mammalian cells and bacteria.38,41,53 The ExoTENPO developed here integrates several technological advances to translate this approach to nanoscale exosomes: (1) Due to its parallelization, the ExoTENPO can precisely sort nanoscale exosomes at flow rates sufficient for clinical use (Φ = 10 mL/h) and is insensitive to clogging from unprocessed clinical samples, as the occlusion of any single nanopore results in diversion of the flow to a nearby pore. (2) By harnessing nanoscale feature sizes to develop a nanoscale immunomagnetic trap, the ExoTENPO sorts each exosome individually based on its labeling by a sufficient number of MNPs, analogous to the microfluidic devices used to selectively sort rare circulating tumor cells.17,30−32 This functionality distinguishes the ExoTENPO from previous microfluidic technologies and microbead systems (e.g., Dynabead, ThermoFisher) that capture exosomes onto functionalized surfaces56 or microbeads,57 wherein the capture purity is set solely by the affinity ligand specificity. Because of our device’s surface marker specific isolation, it is less

CONCLUSIONS We have developed an approach to isolate specific subtypes of exosomes with robust, high throughput nanofluidic sorting. We have used LDA on the exosomal RNA extracted from these exosomes to diagnose cancer in both a mouse model and human subjects and to diagnose precancerous lesions in a mouse model, all in blinded studies. The ExoTENPO was able to properly classify all patient samples and distinguish between healthy subjects and those with pancreatic cancer. The detection of precancerous lesions (PanINs) in the murine model demonstrates the potential of using the ExoTENPO to screen for early stage pancreatic cancer. LDA was used in this study because it is a relatively simple and well understood technique that can discriminate categorical variables using a set 11188

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fabricated by laser micromachining (Universal Laser VLS 3.50) sheets of moisture-resistant polyester film (McMaster-Carr, 0.004 in. thick) (Figure S1). The multiple layers of polycarbonate membranes (N = 6) are stacked using a solvent-resistant double-sided sticky tape (McMaster-Carr). The tape is 100 μm thick, providing a gap of 100 μm between membrane layers, such that exosomes pass through the pores of multiple ExoTENPO membranes. An optically clear cast acrylic sheet (McMaster-Carr) is used to form a reservoir for the input, and the output is made using a polydimethylsiloxane (PDMS) piece, pressure-fit to tygon tubing to connect to a syringe, which collects the flow-through waste of the ExoTENPO. The syringe is connected to a syringe pump (Programmable Syringe Pump, Braintree Scientific) that pulls the sample through the device at flow rates from Φ = 0.1−50 mL/h. To prevent nonspecific binding, the ExoTENPO is coated with pluronic F-127 before use and then flushed with phosphate-buffered saline (PBS). During the coating process, high flow rates (>10 mL/h) are used to flush all air bubbles from the device. Finite Element Simulation. Matlab and Comsol were used for finite element simulation. Because each pore is axially symmetric, we calculated and plotted the simulated magnetic field on the crosssection of the pore. The nanopore was modeled as a disc, with a diameter d = 600 nm and height h = 200 nm, with boundary conditions of zero field at large distances. The magnetophoretic force Fm on an exosome as it passes through a nanopore was calculated by combining the results from the simulation with a simplified model for the exosome. The magnetic moment of the exosome is proportional to the number of MNPs n and the moment mp of the particle (m = n × mp). The model assumes that the magnetic particles are fully magnetized by the externally applied field Bo ≈ 0.4 T ẑ. We assumed mp = 9.27 × 10−3 mA·μm22 and that each targeted exosome has n > 5 MNPs. The number of MNPs (a = 50 nm Fe2O3, Miltenyi) per exosome is calculated based on the limit imposed by steric hindrance on the smallest exosomes, a = 30 nm, assuming 50% maximum loading. Sample Collection (Mice). All mouse work was performed in compliance with institutional and IACUC guidelines. Blood was obtained at sacrifice by cardiac puncture from the right ventricle of tumor-bearing Pdx1-cre, KrasLSL‑G12D, p53L/+, RosaYFP/YFP (KPCY) mice.27 p53L/+, RosaYFP/YFP (PY) and KrasLSL‑G12D, p53L/+, RosaYFP/YFP (KPY) littermates were used as controls. Blood was collected in sodium citrate-coated blood collection tubes (BD Vacutainer). Plasma was isolated by centrifuging the blood at 1600g for 10 min, followed by a second spin at 3000g for 10 min to remove cellular contamination. Sample Collection (Human). Peripheral whole blood was obtained from patients with stage IV pancreatic cancer and from healthy gender-matched controls at the University of Pennsylvania Health System. All patients and healthy donors provided written informed consent for blood donation on approved institutional protocols. Whole blood was drawn in either EDTA (Fisher Scientific), Streck Cell-Free DNA BCT (Streck), or gel serum separation tubes (Fisher Scientific). Plasma and serum were isolated using the following procedures. Within 3 h of blood draw for EDTA and within 12 h of blood draw for Streck, tubes were centrifuged at 1600g for 10 min at room temperature with the brake off. Next, plasma was transferred to a fresh 15 mL centrifuge tube without disturbing the cellular layer and centrifuged at 3000g for 10 min (EDTA) or 4122g for 15 min (Streck) at room temperature with the brake off; this step was repeated with a fresh 15 mL centrifuge tube. After the third spin, plasma was transferred to a fresh 15 mL centrifuge tube, gently mixed, and transferred in 1 mL aliquots to centrifuge tubes and either processed fresh for exosomal RNA or stored immediately at −80 °C for future use. Gel serum separation tubes were stored at room temperature for 30 min after the blood draw. Within 2 h of blood draw, serum tubes were centrifuged at 1000g for 15 min at room temperature. Last, serum was transferred in 1 mL aliquots to cryovials and either processed fresh for exosomal RNA or stored immediately at −80 °C for future use. Cell Culture. Mouse cell lines PD7591, PD483, and PD6910 were generated from pancreatic tumor tissue isolated from Pdx1-cre, KrasLSL‑G12D, p53L/+, RosaYFP/YFP (KPCY) mice.27 The cell lines were cultured in media as previously described.37 The cell lines were

susceptible to copurifying nonexosomal debris known to be present in serum or plasma (e.g., nucleic acid aggregates, cell debris, free-floating proteins).62,63 (3) By using track etching to fabricate the nanopores, precise nanoscale features can be fabricated over a large area for low cost, making the chip manufacturable for medical diagnostic use. (4) The use of LDA to mathematically combine a panel of exosomal RNA biomarkers enabled the accurate diagnosis of cancer, even in the presence of variance in individual exosomal RNA across individual specimens. In comparison to recent work that has shown that pancreatic cancer specific markers can be detected directly from serum or plasma using on-chip plasmonic sensing to diagnose pancreatic cancer,56 we hypothesize that our approach’s higher sensitivity and specificity in the diagnosis of cancer versus healthy patients is because of our more specific exosome isolation and our machine learning approach that combines the measurement of N = 8 exosomal RNA biomarkers. Recent work has shown the benefit of isolating exosomes and analyzing their content of proteins, RNA, and DNA,57 and we believe our machine learning approach can be adapted to analyze protein and DNA content as well. Our prototype ExoTENPO costs approximately $15 per device (material cost: $6, fabrication expense: $6, antibody cost: $3/ mL plasma) with unlimited sample volume processing at a throughput of 10 mL/h. Our device is less expensive than existing exosome isolation kits (Qiagen: $33.15 per sample with limited sample volume of 4 mL, Life Technologies: $16.40 per mL of sample). Because of the ExoTENPO’s all plastic construction, except for the NiFe-coated membranes which can also be fabricated at low cost,38 it is compatible with ultralow cost injection molded plastic production. There are several aspects of the ExoTENPO that can be further developed to expand the system’s functionality to detect a wider range of diseases. The size of the ExoTENPO used in this study (d = 600 nm) could be made smaller or larger to tailor it to alternative extracellular vesicle targets, such as microvesicles (from 200 nm to 1 μm) or oncosomes (≅1 μm). Building on the success of this study, multiple ExoTENPO modules can be integrated to isolate multiple subsets of exosomes targeted using either single surface markers or cocktails of surface markers,33 enabling an increasingly comprehensive view of a cancer disease stage to be measured. Finally, by integrating nanoscale nucleic acid detection34,35,35,36 downstream of the ExoTENPO in future work, we can create a compact, self-contained device for a point-of-care use. Moreover, although this paper has focused on diagnostics, exosomes have been demonstrated in recent years to play a role in intracellular communication.11,58 Our chip’s capability to specifically isolate exosome subpopulations can be an important tool in the study of this exosome-mediated communication. With its high throughput, small footprint, and ease of operation, the ExoTENPO is poised to offer a clinician-friendly tool to translate findings in the emerging field of exosome biomarkers to improve the health of patients.

METHODS/EXPERIMENTAL Chip Fabrication. The ExoTENPO membranes are fabricated by thermally evaporating (Kurt Lesker PVD-75, Singh Nanofabrication Facility, University of Pennsylvania) a 200 nm layer of permalloy (Ni80Fe20) isotropically onto the surface of a track-etched polycarbonate membrane (Whatman). A 30 nm layer of gold is subsequently deposited to prevent oxidation of the permalloy. The ExoTENPO membranes are incorporated into a laminate sheet microfluidic device 11189

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deviation of the fluorescence value of the baseline. Samples were run in triplicate, and melting curves were checked prior to analysis. Linear Discriminant Analysis (LDA). Using Matlab (R2015b), multiple features (genes) from multiple groups (healthy, PanIN, tumor) were incorporated for classification using LDA. The code used to perform LDA is included in Figure S14. The confusion matrix and the LDA plot were made using results from Matlab. Cross validation (N-1) method was used for analysis of the training set data. An independent de novo user blinded validation set was used for the final evaluation. NMR Relaxometry. 50 nm magnetic nanoparticles (MNPs) (Miltenyi) were used to test the performance of the chip. Serial dilution of the MNPs was used to generate a calibration curve (T2 relaxation time versus bead concentration). Next, a suspension of the MNPs was run through the chip, flow through was collected, and its T2 relaxation value measured. All samples were measured using the minispec (Bruker) for T2 relaxation time. Dynamic Light Scattering (DLS). To obtain the size distribution of exosomes in our samples, we used DLS (Zetasizer, Malvern). For each measurement, 300−400 μL samples were measured in triplicate. Captured exosomes were eluted after incubating in an elution buffer (System Biosciences) for 30 min at room temperature. Elution efficiency was not determined as our assay directly lyses exosomes onchip, which does not require elution, release, and recovery of exosomes. RNA Analysis. The size of the exosomal RNA was measured using a BioAnalyzer. Exosomal RNA was measured in the BioAnalyzer using the Agilent RNA Pico chip at the NAPCore Facility at the Children’s Hospital of Philadelphia. The amount and concentration of the exosomal RNA were measured using the Qubit RNA HS Assay Kit (Thermo Fisher Scientific). Statistical Analyses. To generate a predictive model in our analysis of both human and KPCY mouse subjects, we used a cohort of training set data and linear discriminant analysis, carried out using Matlab. We tested our predictive models using an independent, user blinded validation set to avoid the risk of overfitting our data. We evaluated the predictive value using Fisher’s Exact Test. No outlier analysis was performed. Sample size was chosen using Fisher’s Exact Test to measure the P value of our chip’s predictive value.

periodically checked for mycoplasma contamination. All human cell lines were cultured in media recommended by ATCC. Western Blot. Protein was quantified using Qubit Protein Assay kit (ThermoFisher Scientific). Thirty μg of protein was loaded per well and separated by electrophoresis using NuPage 4−12% bis-tris gels (ThermoFisher Scientific). Protein was transferred on to polyvinylidene fluoride (PVDF) membrane (Bio-Rad) and blocked with 5% milk in PBS + 0.05% Tween-20. Primary antibodies used were Purified Anti-ALIX Antibody (634502, BioLegend), Anti-TSG101 Antibody (ab125011, Abcam), Anti-Calnexin Antibody (C4731, Sigma-Aldrich), and Anti-CD61 Antibody (VPA00632, Bio-Rad), followed by horseradish-peroxidase conjugated secondary antibodies (Anti-rabbit HRP (Jackson laboratories, 711-036-152), Anti-mouse HRP (Jackson laboratories, 715-035-150)). Proteins of interest were detected using chemiluminescent substrate (Thermo Fisher Scientific). Exosome Isolation (Ultracentrifugation). Supernatant fractions from confluent cell cultures (48−72h) were collected and centrifuged at 400g for 5 min to remove dead cells and debris. After removal, the supernatant was first spun at 17,000g for 40 min and then at 100,000g for 2 h using Type 70.1 Ti rotor (Optima XPN-100 Ultracentrifuge, Beckman Coulter).55 The exosome pellet was used fresh for downstream analysis. Exosome Isolation (Kit). Supernatant fractions from confluent cell cultures (48−72h) were collected and centrifuged at 400g for 5 min to remove dead cells and debris. Total exosome isolation reagents (from serum, plasma, and cell culture media) from Life Technologies were used. The protocol was followed as suggested by the company. Isolated exosomes were stored at 4 °C for short-term storage or immediately processed for further analysis. Exosome Isolation (ExoTENPO). All samples are vortexed prior to exosome isolation to resuspend any aggregated exosomes, which could be caused by frozen storage condition. Anti-biotin ultrapure microbeads (Miltenyi Biotec) and biotinylated antibodies were used for magnetic labeling. For mice, biotin anti-CD9 antibody (BioLegend) and biotin anti-CD81 antibody (BioLegend, 104903) were used. For humans, biotin anti-human CD9 antibody (eBioscience,13− 0098−80), biotin anti-human CD63 antibody (BioLegend, 353017), and biotin anti-CD81 antibody (custom conjugated by BioLegend) were used. First, biotinylated antibodies (2.5 μL antibody per 20 mL media or per 1 mL plasma) were added to the sample and incubated for 20 min at room temperature with shaking. Then, anti-biotin ultrapure microbeads were added to the samples and incubated for 20 min at room temperature with shaking. The samples were then added to the reservoir of the ExoTENPO chip, and negative pressure was applied by a programmable syringe pump (Braintree Scientific). As the samples were pulled through the chip, magnetically labeled exosomes were captured at the edge of the pores of the chip. The total time to process a 3 mL sample, run through our device at 10 mL/h, including sample preparation time, is 40 min. Exosomal RNA Isolation. Total exosomal RNA and protein isolation kit (Life Technologies) was used for RNA extraction from isolated exosomes. For the exosomes captured on-chip, denaturing solution was added to the chip, and the chip was incubated for 5 min on ice. Then, the lysed solution was taken off-chip for acid-phenol separation and washing steps using a spin column. The exosomal RNA was eluted in a small volume (∼30 μL) and stored at −80 °C or processed immediately for further analysis. Polymerase Chain Reaction (PCR). RT-PCR was first performed using exosomal RNA. PrimeScript RT Reagent Kit (Clontech) was used for RT-PCR. Using the kit, the exosomal RNA was mixed with reagents and run in a T100 Thermal Cycler (Bio-Rad) as per the manufacturer’s protocol. The qPCR master mix that consists of SsoAdvanced Universal SYBR Green Supermix (Bio-Rad), primers (Integrated DNA Technologies), and water was made at a 5:0.5:3.5 ratio, and 9 μL of the master mix was added to each well, followed by 1 μL of cDNA. For every qPCR experiment, 40 cycles were run using a CFX384 Touch Real-Time PCR machine (Bio-Rad). The threshold level was determined automatically using the default settings on the CFX384 where the software sets the threshold at 10 times the standard

ASSOCIATED CONTENT S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsnano.7b05503. ExoTENPO characterization, mechanism schematic, layer-by-layer images, SEM images, Western blot, control/specificity data, Bioanalyzer data, RNA yield/ expression level comparison, demographic information on clinical samples, and a MATLAB code for machine learning (PDF)

AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected]. ORCID

David Issadore: 0000-0002-5461-8653 Author Contributions

J.K. helped conceive and perform all experiments in this study as well as prepare the manuscript and figures. N.B., S.Y., N.O., T.B., N.A., A.S., N.M.A., L.M., M.O., C.R., and J.R. helped conceive and perform experiments on murine and clinical testing of our device. N.O and A.S. helped fabricate the ExoTENPO devices. B.S. and E.C. helped conceive experiments in this study as well as prepare the manuscripts and figures. D.I. 11190

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conceived and oversaw all aspects of this study and prepared the manuscript. All authors reviewed the manuscript. Notes

The authors declare the following competing financial interest(s): David Issadore is a founder of and holds shares of Chip Diagnostics.

ACKNOWLEDGMENTS Funding was provided by the Pennsylvania Department of Health Commonwealth Universal Research Enhancement Program, the National Institute of Health: 1R21CA18233601A1 to D.I. and J.K., R01-CA169123 to B.Z.S., F31CA177163-01A1 to N.M.A., F32CA196120 to N.B., R01CA207643, Pancreatic Cancer Action Network Translational Research Award, Abramson Cancer Center Translational Centers of Excellence to E.C., and in part by the Penn Center for Molecular Studies in Digestive and Liver Diseases (P30DK050306) from the National Institute of Diabetes and Digestive and Kidney Diseases. D.I. was supported by an American Cancer Society - CEOs Against Cancer - CA Division Research Scholar Grant, (RSG-15-227-01-CSM). We thank Dr. Bin Wu from Dr. Wei Guo's lab for assistance in performing ultracentrifugation. REFERENCES (1) Distler, M.; Aust, D.; Weitz, J.; Pilarsky, C.; Grutzmann, R. Precursor lesions for sporadic pancreatic cancer: PanIN, IPMN, and MCN. BioMed Res. Int. 2014, 2014, 474905. (2) Morris, J. P.; Cano, D. A.; Sekine, S.; Wang, S. C.; Hebrok, M. beta-catenin blocks Kras-dependent reprogramming of acini into pancreatic cancer precursor lesions in mice. J. Clin. Invest. 2010, 120, 508−520. (3) Suram, A.; Kaplunov, J.; Patel, P. l.; Ruan, H.; Cerutti, A.; Boccardi, V.; Fumagalli, M.; Di Micco, R.; Mirani, N.; Gurung, R. L.; Hande, M. P.; d’Adda di Fagagna, F.; Herbig, U. Gurung. Oncogeneinduced telomere dysfunction enforces cellular senescence in human cancer precursor lesions. EMBO J. 2012, 31, 2839−2851. (4) Mullerat, J.; Deroide, F.; Winslet, M. C.; Perrett, C. W. Proliferation and p53 expression in anal cancer precursor lesions. Anticancer Res. 2002, 23, 2995−2999. (5) Hammes, L. S.; Tekmal, R. R.; Naud, P.; Edelweiss, M. I.; Kirma, N.; Valente, P. T.; Syrjänen, K. J.; Cunha-Filho, J. S. Up-regulation of VEGF, c-fms and COX-2 expression correlates with severity of cervical cancer precursor (CIN) lesions and invasive disease. Gynecol. Oncol. 2008, 110, 445−451. (6) Lang, J. M.; Casavant, B. P.; Beebe, D. J. Circulating tumor cells: getting more from less. Sci. Transl. Med. 2012, 4, 141ps13−141ps13. (7) Ko, J.; Carpenter, E.; Issadore, D. Detection and isolation of circulating exosomes and microvesicles for cancer monitoring and diagnostics using micro-/nano-based devices. Analyst 2016, 141, 450− 460. (8) Taylor, D. D.; Gercel-Taylor, C. MicroRNA signatures of tumorderived exosomes as diagnostic biomarkers of ovarian cancer. Gynecol. Oncol. 2008, 110, 13−21. (9) Shao, H.; Chung, J.; Balaj, L.; Charest, A.; Bigner, D. D.; Carter, B. S.; Hochberg, F. H.; Breakefield, X. O.; Weissleder, R.; Lee, H. Protein typing of circulating microvesicles allows real-time monitoring of glioblastoma therapy. Nat. Med. 2012, 18, 1835−1840. (10) Melo, S. A.; Luecke, L. B.; Kahlert, C.; Fernandez, A. F.; Gammon, S. T.; Kaye, J.; LeBleu, V. S.; Mittendorf, E. A.; Weitz, J.; Rahbari, N.; Reissfelder, C.; Pilarsky, C.; Fraga, M. F.; Piwnica-Worms, D.; Kalluri, R. Glypican-1 identifies cancer exosomes and detects early pancreatic cancer. Nature 2015, 523, 177−182. (11) Costa-Silva, B.; Aiello, N. M.; Ocean, A. J.; Singh, S.; Zhang, H.; Thakur, B. K.; Becker, A.; Hoshino, A.; Mark, M. T.; Molina, H.; Xiang, J.; Zhang, T.; Theilen, T. M.; Garcia-Santos, G.; Williams, C.; 11191

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DOI: 10.1021/acsnano.7b05503 ACS Nano 2017, 11, 11182−11193

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DOI: 10.1021/acsnano.7b05503 ACS Nano 2017, 11, 11182−11193