Research ArticleCancer

Multiparametric plasma EV profiling facilitates diagnosis of pancreatic malignancy

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Science Translational Medicine  24 May 2017:
Vol. 9, Issue 391, eaal3226
DOI: 10.1126/scitranslmed.aal3226

A signature achievement

Pancreatic ductal adenocarcinoma is one of the deadliest types of tumors, in part because it is usually detected at a late stage. To facilitate the diagnosis of this tumor, Yang et al. developed a multiplexed plasmonic assay to evaluate extracellular vesicles in patient plasma for protein markers associated with the presence of pancreatic cancer. The authors identified a five-marker signature that yielded the most accurate diagnosis. To test their assay, the researchers analyzed samples from patients with pancreatic cancer and other types of pancreatic disease, as well as healthy controls, and confirmed the accuracy of their signature in prospectively collected samples.


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.


Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer death in the United States, with a 5-year survival rate below 10%. Most newly diagnosed patients’ (>80%) tumors are considered unresectable (1). Earlier detection could increase survival by an estimated 30 to 40% (2), and more reliable and real-time assessment of treatment effects could prolong survival and/or improve quality of life. Detecting serum concentrations of CA 19-9 is currently the best established blood test for PDAC and has a pooled sensitivity of 75.4% [95% confidence interval (CI), 73.4 to 77.4%] and a specificity of 77.6% (CI, 75.4 to 79.7%) (3). Although often used to follow treatment response, CA 19-9 is a poor biomarker for early detection, commonly rises late in the disease, and may be elevated in nonmalignant conditions such as biliary obstruction and pancreatitis (4). Recent modeling studies on assay performance cite a minimum sensitivity of 88% and a specificity of 85% to prolong patient survival and demonstrate cost effectiveness (2). Various approaches to achieving this are being explored (5, 6), including the use of CA242 (3), circulating tumor cells (7), circulating tumor-derived extracellular vesicles (tEVs), which include exosomes (8), metabolites (9) and proteomic analyses (10, 11), and circulating DNA (12). Beyond the inherent technical challenges of these advanced analyses, a key question is whether early reported findings can be validated independently in larger sets of patients.

tEVs offer an attractive approach to monitoring cancers using “liquid biopsies.” tEVs are relatively more abundant than other circulating biomarkers, are structurally more stable, and contain protein and mRNA profiles that highly reflect parental cancer cells (1315). Experimental studies have shed light on the composition and functional roles of tEVs (16, 17) for (i) diagnosis (13, 18, 19), (ii) long-range communication (16), and (iii) host cell interaction (20). Rapid and accurate analysis of tEVs in clinical samples is often hampered by three practical challenges: lengthy isolation procedures requiring ultracentrifugation to isolate tEVs, general unavailability of ultrasensitive assay systems to analyze large clinical cohorts for multiple markers, and specific cancer markers that separate tEVs from host cell EVs. Here, we developed an advanced plasmonic sensing system for higher-throughput analysis of clinical samples to directly address the shortcomings often found in translational clinical analyses. The basic operating principle relies on measuring spectral shifts of resonant light transmission through periodically arranged gold nanopores to which tEVs are captured by immunoaffinity. In proof-of-principle cancer experiments, we showed that tEVs can be detected by plasmonic sensors (14). However, the original prototype was manually operated and had limited throughput and chip production rates, thus preventing widespread clinical use. As a result, we devised a multiparametric system incorporating large numbers of sensing arrays (>100 sensing spots) and automatic operation to enable routine clinical sample analyses. Intrigued by recent reports (8, 21), we set out to determine key protein profiles of tEVs in 135 patients undergoing surgery for pancreatic pathologies. Motivated by clinical needs, we were particularly interested in defining practical and reliable tEV marker sets for PDAC diagnosis.


Nanoplasmonic sensors for high-throughput, sensitive analyses of EVs

Figure 1 summarizes the working principle of the nanoplasmonic sensor (NPS) assay, specifically designed for clinical workflows, small clinical sample amounts, and high-throughput detection. The sensor chip contains periodically arranged nanopores (200 nm in diameter and 500 nm in periodicity) patterned in a 100-nm-thick gold film. The function of the pores is to transmit light shone onto the gold surface (Fig. 1A). When EVs are bound in the vicinity of these pores via specific antibodies, the wavelength of the transited light shifts to red. It is this red shift that is detected by sensors and reflects the amount of bound EVs (Fig. 1, B and C).

Fig. 1. Working principle of the plasmon sensor chip (NPS) for tEVs.

(A) EV binding to the nanopore surface via monoclonal antibody (mAb) immobilized on the gold surface causes a spectral shift of light transmitted through the nanopores. (B) The spectral shift of resonance peak in light transmission is measured to quantify the amount of EVs captured on the nanopore surface. a.u., arbitrary unit. (C) Scanning electron micrographs show the periodically arranged nanopore array and EVs captured on the surface. Each nanohole has a diameter of 200 nm and a periodicity of 500 nm.

The sensor chip is easily scalable to larger arrays of more than 1000 sensing sites; multiple sensor chips can be made through batch fabrication processes (see Materials and Methods for details). We prepared a customized chip with 100 detection sites to yield data for 25 antibodies/markers in quadruplicate. Antibody and EV solutions were printed on the sensor chip in spots as small as 100 nl through a molecular printing method. A piezoelectric microscope stage was incorporated into the system for scanning sensing arrays and collecting transmission spectra. Both printing and measurements are operated automatically to improve assay throughput and reduce variation among users. Overall, the smaller chip size, higher spot density, and smaller measurement volumes resulted in a 25× increase in sensitivity compared to a previous prototype (120 μl for 12 markers versus 10 μl for 25 markers). Figure S1 shows the NPS chip with 100 sensing arrays and the integrated setup optimized for processing clinical samples.

Correlation between tEV composition in PDAC models and that of parental cells

A number of putative cell-associated PDAC markers have been described for individual patients, but using single markers in entire cohorts generally has insufficient sensitivity or specificity. Proteome analyses have identified soluble markers (11, 2224), and profiling studies have identified cell surface (2527) or exosomal markers (8, 28, 29). To calibrate and validate the new plasmonic sensing system, we investigated 15 putative cancer and EV markers (table S1) by performing flow cytometry on whole pancreatic cancer cells (fig. S2). On the basis of the cell data, we eliminated some of the non–cancer-specific markers and performed NPS measurements on tEVs (Fig. 2A). Beyond the commonly used PDAC cell lines, we also investigated 11 patient-derived tumor xenograft (PDX) models of PDAC, metastatic PDAC, and intraductal papillary mucinous neoplasm (IPMN) (Fig. 2A). Our data show good correlation between expression patterns seen in whole cells and tEVs (Spearman correlation coefficient r = 0.86 for 1157-PDAC, 1222-PDAC, 1247-PDAC, and 1494-PDAC; Fig. 2B). The tEV assays by the NPS chip are on the order of 102 more sensitive than the gold standard enzyme-linked immunosorbent assay (ELISA) for this analysis (Fig. 2C).

Fig. 2. In vitro profiling of tEV markers on cell line–derived EVs.

(A) The molecular expression of cancer markers (EGFR, EPCAM, HER2, MUC1, GPC1, WNT2, and GRP94) and EV markers (CD63, RAB5B, and CD9) was characterized on EVs derived from 4 cancer cell lines and 11 PDX cell lines including PDAC, metastatic PDAC (PDAC-MET), and IPMN. (B) Correlation of protein expression between EVs and their parental cell lines (1157-PDAC, 1222-PDAC, 1247-PDAC, and 1494-PDAC). (C) Sensitivity comparison between NPS and the gold standard ELISA. The responses were normalized against the values of highest concentrations.

Establishing a PDAC tEV panel

We next collected plasma from 32 patients enrolled in a training cohort involving 22 cases of PDAC and 10 healthy controls (Table 1). Figure 3A summarizes the chosen tEV markers for each patient, including pan-cancer markers (EGFR, EPCAM, HER2, and MUC1) and putative PDAC markers [GPC1, WNT2, and GRP94 (30)]. Using receiver operating characteristic (ROC) analyses, we determined sensitivity, specificity, and accuracy for each marker individually and also in combination (Fig. 3B and Table 2). We observed that no single marker achieved sufficiently high sensitivity and specificity. Therefore, we reasoned that a combination of multiple markers would be necessary. A previously identified generic quad marker cancer signature (31) (EGFR, EPCAM, HER2, and MUC1) had high sensitivity (91%), specificity (100%), and accuracy (94%). When we replaced HER2 with putative PDAC markers (GPC1 and WNT2), we further improved the sensitivity and specificity (Table 2). This PDACEV signature, representing an unweighted sum of EGFR, EPCAM, MUC1, GPC1, and WNT2 signals, had an accuracy of 100% in this training cohort (Fig. 3, C and D). Because of the limited sample size (n = 32), we also tested all four and five marker combinations in the prospective cohort described below.

Table 1. Summary of patient cohorts.
View this table:
Fig. 3. Molecular profiling of plasma EV for a training cohort.

(A) Putative cancer markers (EGFR, EPCAM, HER2, and MUC1) and PDAC markers (GPC1, WNT2 and GRP94) were profiled on EVs collected from 22 PDAC patients and 10 healthy controls. (B) ROC curves were calculated for single protein markers as well as for the PDACEV signature combination to determine optimum EV threshold values. AUC, area under the curve. (C) A combined marker panel (EGFR, EPCAM, MUC1, GPC1, and WNT2) was established as a PDACEV signature that showed 100% accuracy for the training cohort in distinguishing PDAC from healthy controls. P value was determined by Mann-Whitney test. ****P < 0.0001. (D) A waterfall plot shows the PDACEV signature signals sorted from high (left) to low (right). Each column represents a different patient sample (red, malignant; blue, benign).

Table 2. Statistical analyses of EV markers for training and prospective cohorts.

Ninety-five percent CIs are indicated in parentheses. NA, not applicable.

View this table:

A number of observations were of particular interest. First, some of the chosen markers highly expressed in EVs did not provide diagnostic information (Rab5b, CD9, and CD63; fig. S3) and were thus eliminated from the ensuing prospective study. Second, GPC1 was not specific for PDAC in our cohort and had a lower accuracy as a single marker than marker combinations, similar to other markers tested. These findings did not change by using alternative commercially available GPC1 antibodies, all of which were validated before use (table S1). Third, other putative PDAC markers such as WNT2 showed better accuracy than GPC1.

Validation cohort

We next analyzed a prospective cohort of 43 patients undergoing surgery for pancreatic (n = 35) or other abdominal indications (n = 8, age-matched control group). In all 35 patients operated for pancreatic indications, tissue was available for clinical pathology interpretation (n = 22 for PDAC, n = 8 for pancreatitis, and n = 5 for benign cystic tumor). We obtained 2 to 10 ml of plasma from each patient on the day of or immediately preceding surgery, and NPS measurements were performed using identical markers from our training cohorts (see Materials and Methods for details).

Figure 4A summarizes the performance of the PDAC markers in differentiating PDAC from pancreatitis, benign, and control patient groups. Analyzing the heat map of EV markers once again demonstrated that no single patient had similar markers elevated. Rather, it was the combination of the five markers comprising the PDACEV signature that resulted in an overall accuracy of 84%. In this prospective cohort, the PDACEV signature (EGFR, EPCAM, MUC1, WNT2, and GPC1) identified in the training cohort showed a sensitivity of 86% (CI, 65 to 97%) and a specificity of 81% (CI, 58 to 95%; Fig. 4B and Table 2), whereas total EV concentrations were not significantly different between the groups (Dunn’s multiple comparisons test, P = 0.16 for PDAC and pancreatitis; P = 0.78 for PDAC and control) (Fig. 4C). Furthermore, the expression of GPC1 was not significantly different in PDAC relative to pancreatitis (P = 0.31) but was slightly higher in PDAC when compared to the control group (median values of 0.20 for PDAC and 0.02 for the control group; P = 0.018) (Fig. 4D). Figure 5 displays the experimental data of single markers and combinations as a waterfall plot. Table 2 summarizes the diagnostic accuracies of all markers and combinations in this prospective cohort.

Fig. 4. The PDACEV signature differentiation of PDAC patients from pancreatitis and control patient groups.

(A) Heatmap analysis of EV markers. The PDACEV signature is defined as a combined marker panel of EGFR, EPCAM, MUC1, GPC1, and WNT2. (B to D) The established PDACEV signature signals (B), EV concentrations (C), and single GPC1 signal (D) as measured for plasma EVs collected from 22 PDAC patients, 8 with pancreatitis, 5 with benign cystic tumors, and 8 age-matched controls. Pairwise comparison P values were determined by the Dunn’s multiple comparisons test. *P < 0.05, ***P < 0.001, ****P < 0.0001. n.s., not significant.

Fig. 5. Distribution of EV protein marker signals.

Waterfall plots show EV protein content for each of the different biomarkers sorted from high (left) to low (right). Each column represents a different patient sample (red, PDAC, n = 22; blue, pancreatitis, n = 8; green, age-matched controls and benign cystic tumors, n = 13).

We next correlated EV analyses to clinical gold standard serum biomarkers (CA 19-9 and CEA) in patients with PDAC. The PDACEV signature was not correlated with either CA 19-9 (Spearman correlation coefficient r = −0.28; P = 0.26) or CEA (r < 0.001; P > 0.99) (Fig. 6A). In our cohort, only 61% of PDAC patients (11 of 18) showed an increased concentration of CA 19-9 (>37 U/ml, threshold value used in clinic), whereas 89% (16 of 18) had high PDACEV values (>0.87 NPS signal; Table 2). For CEA, only 17% of PDAC patients (3 of 18) were positive (>5 ng/ml; Fig. 6B). Finally, we compared the PDACEV signature against tumor size, showing a modest correlation for the signature (r = 0.58; P = 0.018) and little correlation (r = −0.09; P = 0.62) between EV counts and tumor size (Fig. 6C and fig. S4).

Fig. 6. Comparison of EV analyses with conventional clinical metrics.

Correlation of the PDACEV signature values with serum biomarkers [CA 19-9 (A) and CEA (B)] and the tumor diameter (C) for patients with PDAC. tx, treatment. The dashed red lines indicate the threshold values for positivity (CA 19-9, 37 U/ml; CEA, 5 ng/ml; PDACEV signature, 0.87).

To further expand the clinical use of tEV analyses, we also studied several additional patient cohorts (n = 69; Table 1; Fig. 7 and figs. S5 and S6): (i) PDAC treated with neoadjuvant regimen (n = 24), (ii) IPMN (n = 14), (iii) other gastrointestinal (GI) cancers mimicking the symptoms of pancreaticoduodenal cancers (n = 11), (iv) pancreatic neuroendocrine tumors (NETs; n = 12), and (v) benign cystic tumors (n = 5). Patients with PDAC treated with neoadjuvant regimen had lower EV signatures (median values, 1.70 versus 0.86; Mann-Whitney test, P = 0.015) (fig. S7A) as a group compared to the untreated PDAC group, likely reflecting the smaller tumor mass and/or favorable treatment response.

Fig. 7. EV analyses for patients with different types of pancreatic diseases.

The PDACEV signature values were measured for patient cohorts (n = 103) including (i) PDAC without treatment (n = 22), (ii) PDAC treated with neoadjuvant regimen (n = 24), (iii) IPMN (n = 13), (iv) other GI cancers mimicking the symptoms of pancreaticoduodenal cancers (n = 11), (v) pancreatic NET (n = 12), (vi) pancreatitis (n = 8), (vii) benign cystic tumors (n = 5), and (viii) age-matched controls (n = 18).

We also studied a number of cases of IPMNs, which grow within the pancreatic ducts and are characterized by the production of thick mucinous fluid. IPMNs are important because some of them progress to invasive cancer and may therefore represent windows of opportunity to treat before aggressive and difficult-to-manage cancer develops. Our cohort contained 11 cases of intermediate and high-grade IPMN and 2 cases of low-grade IPMN (Fig. 7 and Table 1). As shown in fig. S7B, IPMN had an elevated PDACEV signature compared to age-matched controls (Dunn’s multiple comparisons test, P < 0.0001), but it was lower compared to PDAC (P = 0.022).

The validation cohort included a limited number of other pancreatic cancers or cancers that can mimic pancreatic symptomatology. These included NETs and gastroduodenal cancers. Again, although the numbers are limited, most of the malignancies tested positive for some of the EV markers (for example, among 23 patients, 18 were positive for EGFR and/or EPCAM). These findings are in line with other observations. For example, EPCAM has been evaluated as a CTC detection marker in NET populations (32, 33). Of particular interest was the fact that 9 of 12 NETs tested positive for SSTR2 expression on EVs, whereas all PDAC patients (n = 22) and age-matched healthy controls (n = 8) were negative for SSTR2 with a threshold value of 0.15 (Dunn’s multiple comparisons test, P < 0.0001 between PDAC and NET; P = 0.0018 between NET and control) (fig. S7C). Finally, we investigated a limited number of patients with benign mucinous tumors but no detectable malignancy. In these cases, we observed PDACEV signatures similar to the age-matched control group (Mann-Whitney test, P = 0.35; fig. S7D).


EVs are attractive as circulating biomarkers given their abundance, relative stability, and similar molecular makeup to parental cells (1315). Despite these apparent advantages, it has been difficult to define single tumor-specific EV markers (mRNA, DNA, or protein) (8, 28, 34), validate purported malignancy biomarkers in larger patient cohorts (8, 13, 34), implement lengthy purification procedures (ultracentrifugation) into the clinical workflow (13), and commercialize cost-effective technologies. Here, we show in a sizable pancreatic data set that single EV protein biomarkers are unlikely to be sufficiently accurate to improve patient management. No individual putative protein tEV marker (EGFR, EPCAM, MUC1, GPC1, or WNT2) yielded sensitivities above 86% and specificities above 81% to be considered cost-effective (2). Many had much lower sensitivities/specificities, including GPC1, despite previous studies (5, 8); unfortunately, the previously used GPC1 antibody is no longer commercially available. Therefore, there is a possibility that the discrepancy could be attributed to the antibody used in the study.

On the basis of the hypothesis that tumoral heterogeneity will require multiplexed biomarkers for clinical use, we set out to define protein signatures representative of epithelial and pancreatic cancers (25). We initially surveyed about 50 proteins of potential interest and discarded all but 10 after feasibility studies in PDAC cell lines and PDX models. These 10 EV markers included 7 tEV markers (EGFR, EPCAM, MUC1, HER2, GPC1, WNT2, and GRP94) and 3 pan EV markers (CD63, CD9, and Rab5b). The pan EV markers were excluded from the tumor diagnostic marker panel and were solely used to confirm the presence of EVs in a given sample. From an initial training data set, we further refined the markers to five essential ones, which then constituted our PDACEV signature (EGFR, EPCAM, MUC1, GPC1, and WNT2). The signature was defined as the unweighted sum of each marker expression, with a score of >0.85 suggesting PDAC. It is conceivable to further improve on this panel by identifying additional molecular tumor markers present on the EV surface. A number of proteomic approaches have been used to identify putative markers, but validation work remains to be done. It would also be attractive to expand the panel to intravesicular markers such as mutant KRAS protein, but this would require EV lysis and further NPS assay optimization.

Applying the above PDACEV signature to 43 patients, we showed an overall sensitivity of 86% for detecting PDAC and a specificity of 81% for differentiating PDAC from other pancreatic diseases (Table 2). The accuracy was 84% (CI, 69 to 93%). The relatively high accuracy is most likely attributed to the selection of protein EV markers, the surgical patient cohorts enrolled, and ease of measurements, resulting in reduced analytical failures. The last point of particular interest is that existing EV analyses are cumbersome and often require large sample amounts. In contradistinction, we set out to develop a miniaturized sensing technology with an automated microarray spotter and scanning stage to perform measurements at scales that are clinically feasible and affordable. The NPS measurements performed here require ~10 min of measurement/analysis time and currently cost $60 (chip cost, $42; antibody cost, $18) per patient sample. Because the majority of current costs are driven by manual manufacture of chips and antibodies, it is expected that real costs will scale downward greatly with bulk fabrication. The current limits of the NPS technology as developed in this study are (i) the need for EV purification and concentration before measurement, (ii) lower sensitivity for intravesicular markers, (iii) the need for high-quality antibodies that are necessary for capture, and (iv) the composition of the marker panel. With further optimization and commercialization, all these points could be addressed and further improved.

A number of previous studies have investigated tEV as a diagnostic cancer marker by both protein and nucleic acid analyses (8, 1315). Remaining questions include (i) whether these results hold up in larger patient cohorts and (ii) how cost-effective and practical are newer analytical techniques. For example, Melo et al. (8) explored the use of GPC1 as a single marker for detection of PDAC from EVs. Similarly, several studies have explored the serum proteome of PDAC (2224) with the goal of providing more advanced diagnostic tools to guide clinicians. So far, measurement of tEV appears to be a promising venue for pancreatic diagnoses.

The current study was designed as a feasibility study to focus on some of the pressing questions in surgical oncology. Future studies should expand tEV analysis to assess treatment efficacy. Although the current study was not designed to investigate this, such work has been done for other cancers (14, 15, 35). For example, we have shown that longitudinal tEV profiling is feasible and can be informative in treatment assessment (14, 15, 35). Long-term efforts should also include longitudinally analyzing high-risk subjects for PDAC development, which will require larger data sets and multiyear follow-ups.


Study design

We used data collected from a pilot study at Massachusetts General Hospital (MGH) to optimize use of the NPS technique for tEV detection in plasma and to identify useful biomarker combinations and their detection thresholds as a training data set. To more accurately assess the biomarker performance, we obtained an independent data set using de-identified specimens from patients with pancreatic-related diseases collected at MGH. Before processing clinical samples, we performed exhaustive analysis of known EV protein biomarkers relevant to PDAC in patient-derived cell lines. In addition, extensive correlation and optimization studies were performed to validate NPS measurements.

Cell lines

AsPC-1, MIA PaCa-2, PSN-1, and PANC-1 cell lines were purchased from the American Type Culture Collection. AsPC-1 and PSN-1 cells were maintained in RPMI 1640 medium. MIA PaCa-2 and PANC-1 cells were maintained in Dulbecco’s modified Eagle’s medium. All cell line media were supplemented with 10% fetal bovine serum, 100 IU of penicillin, and streptomycin (100 μg/ml). PDAC PDX cell lines were provided by C. Fernandez del Castillo and were all maintained in a 50:50 mix of Dulbecco’s modified Eagle’s and Ham’s F-12 medium supplemented as above.

Selection of markers

Several PDAC proteomic studies have been described in the literature (10, 11, 2224, 29) or are available online (;; These literature sources were analyzed to define EV marker candidates. To derive the marker set, we surveyed these available databases, three vesicle databases (Vesiclepedia, EVpedia, and ExoCarta), and the literature for published markers. The putative “hits” were then screened using commercially available antibodies (see table S1). We eliminated targets that were not specific for cancer cells, yielded only duplicative information, or were primarily intravesicular proteins, which we were not able to capture efficiently. From the initial 50 candidate markers, we selected 10 after feasibility studies with PDAC cell lines and PDX models (fig. S2). We decided to take forward seven tumor markers, all of them vesicle surface markers that can be used for chip capture. In addition, we assayed for three ubiquitous EV markers: CD9, CD63, and Rab5b.

Antibody and biotinylation

All antibodies used in these studies are listed in table S1. For biotinylation, all antibodies [50 μg in 100 μl of phosphate-buffered saline (PBS)] were first passed through 0.5 ml 7K MWCO Zeba Spin Desalting columns (89882, ThermoFisher) to remove sodium azide. EZ-Link Sulfo-NHS-LC-Biotin (21327, ThermoFisher) was used for antibody biotinylation according to the manufacturers’ instructions. Briefly, antibodies were mixed with a 20-fold molar excess of 10 mM biotin for 30 min at room temperature. Excess biotin was then removed using a second Zeba Desalting column. Antibody concentration was checked using a NanoDrop spectrophotometer (ThermoFisher).

Flow cytometry

Antibodies were tested and compared to EV signals from NPS using flow cytometry. On the day of EV collection from cell lines, a portion of the remaining cells were trypsinized for flow cytometry. Cells (500,000 to 1,000,000 per condition) were fixed in 4% paraformaldehyde in PBS (15710-S, Electron Microscopy Sciences) for 10 min at room temperature. Cells were washed twice with PBS plus 0.5% bovine serum albumin (BSA). Antibodies were diluted to 10 μg/ml in 100 μl of PBS plus 0.5% BSA and incubated with cells for 1 hour at room temperature. Cells were washed twice with PBS plus 0.5% BSA and then incubated with appropriate Alexa Fluor 488 secondary antibody diluted 1:1000 in PBS plus 0.5% BSA for 30 min at room temperature. Cells were washed twice with PBS plus 0.5% BSA. Fluorescent signal was measured using a FACSCalibur flow cytometer (BD Biosciences) and compared to appropriate isotype controls and secondary antibody–only signal using the following formula: (signal primary antibody − signal isotype control)/signal secondary antibody.

EV isolation from cell culture

Cells were grown for 48 hours in normal growth medium supplemented with 5% EV-depleted fetal bovine serum (A2720801, ThermoFisher). Conditioned medium was collected in 50-ml tubes and centrifuged at 300g for 10 min. Medium was filtered through a 0.22-μm cellulose acetate vacuum filter (430767, Corning) and then aliquoted into ultracentrifuge tubes (344058, Beckman). Medium was centrifuged at 100,000g for 70 min to pellet EVs. The pellet was washed with PBS and repelleted by centrifugation at 100,000g for 70 min. EVs were resuspended in an appropriate volume of PBS and stored at −80°C until NPS measurement.

Sample collection

The current study was designed to prospectively obtain fresh samples and then correlate them with pathological and clinical information. All clinical data were entered into a unified database and used for blinded analyses by the biobank coordinator at MGH. The biospecimen collection was optimized for EV analysis and included the following steps: (i) collect whole blood into one 10-ml purple-middle EDTA tube, (ii) mix blood by inverting the tube 10 times, (iii) store vacutainer tubes upright at 4°C and process within 1 hour of blood collection, (iv) centrifuge blood samples for 10 min at 400g at 4°C, (v) collect the plasma layer in a 15-ml conical tube with a pipette without disturbing the buffy coat, (vi) centrifuge the plasma layer for 10 min at 1100g at 4°C, (vii) pipette the plasma into a 15-ml labeled conical tube, and (viii) store at −80°C until processing.

EV isolation from plasma

Plasma was thawed, aliquoted into ultracentrifuge tubes, and diluted to 30- to 35-ml total volume in PBS. Plasma was initially centrifuged at 14,000g for 20 min to pellet cell debris. Cleared supernatant was passed through a 0.22-μm polyvinylidene difluoride (Millipore) syringe filter into an ultracentrifuge tube. EVs were then pelleted by ultracentrifugation at 100,000g for 70 min. The pellet was resuspended in PBS and centrifuged again at 100,000g for 70 min. The final EV pellets were resuspended in 300 μl of PBS and stored at −80°C until NPS measurement.

EV size measurements

Nanoparticle tracking analysis (Nanosight) was used to assess EV size and concentration. Measurements were done as reported in the literature (36). Briefly, samples were diluted in PBS (generally a 1:100 dilution). Five 30-s videos were recorded using the following settings for all measurements: threshold, 1482; gain, 680. Videos were processed, and the highest and lowest EV concentrations were excluded.

NPS fabrication

We used interference lithography to prepare NPS devices (fig. S1). First, periodic nanohole patterns were made on a double-polished 4-inch (~10 cm) Si wafer coated with a 125-nm silicon nitride (SiN) layer. The patterned wafer was dry-etched using reactive ion etching to create nanoholes in the SiN layer. In this step, only a partial layer was etched to protect the front Si surface from the subsequent silicon etching with potassium hydroxide (KOH). The opposite Si backside was lithographically patterned to define sensing sites and wet-etched with KOH at 80°C. Patterned wafers were diced into individual NPS chips, with each chip containing 100 (10 × 10) measurement sites. After removing the remaining SiN layer, a 100-nm Au film with a 2-nm Ti adhesion layer was directly deposited on the patterned SiN side. After the EV assays, the metal films were removed by Au etchant and hydrogen fluoride (HF) solutions to regenerate chips. After cleaning the patterned Si templates, fresh metal films were deposited on the regenerated Si templates.

NPS measurement

The fabricated Au chip was first incubated with a 1:3 mixture of 10 mM linear polyethylene glycol (thiol-PEG-biotin, 1 kDa, Nanocs Inc., and methyl-PEG-thiol, 0.2 kDa, Fisher Scientific Inc.) overnight at room temperature. After washing in PBS, the chip was secondarily incubated with neutravidin (Thermo Scientific; 50 μg/ml in PBS with 0.2% BSA) for 40 min at room temperature. Finally, after washing, 0.5 μl of biotinylated antibodies (10 μg/ml in PBS with 0.2% BSA) was added to individual nanopore arrays by using a microarray spotter (DigiLab Inc.) and incubated for 40 min at room temperature with humidity. The antibody-conjugated chip was washed in PBS and then measured with a spectrometer (USB4000-UV-VIS-ES, Ocean Optics Inc.) to obtain a baseline spectrum.

For EV detection, EV samples (0.5 μl, in 1% BSA) were spotted onto individual sensor arrays using the microarray spotter and incubated in a humidity chamber for 50 min at room temperature. The chip was washed with PBS to remove unbound EVs, and light transmission of each nanopore array was measured. A custom-built software program (MATLAB R2015a, MathWorks Inc.) was used to analyze spectral shifts after EV binding. A set of control arrays with isotype control antibodies was used to measure signals due to nonspecific binding; these background signals were subtracted from the positive arrays.


Between 2015 and 2016, 135 patients underwent surgical resection of pancreatic neoplasms or other abdominal abnormalities. Through an Institutional Review Board–approved protocol at MGH (principal investigator: C.F.d.C.), blood samples were acquired. All samples were anonymized, and only age, gender, medical history, and final pathological diagnosis were recorded. All samples were processed by operators blinded to the sample type.


The Spearman correlation coefficient was used to quantify the correlations between different variables. Group differences were tested using the nonparametric Mann-Whitney test for two groups and the Kruskal-Wallis test for more than two groups; P values for pairwise comparisons were obtained using the Dunn’s multiple comparison test. ROC curves were constructed for individual markers and selected marker combinations to describe the accuracy of detecting cancer. The cutoff points were selected using Youden’s index, which maximizes the sum of sensitivity and specificity. We used data from the training cohort (n = 32) to select the optimal cutoff points associated with individual markers and marker combinations and then evaluated the sensitivity, specificity, and accuracy of predicting tumor status associated with the optimal cutoff points using data from the prospective cohort (n = 43). Selection of marker combinations was informed by literature, biological information, and data-driven statistical procedures. One set of markers was selected through fitting the least absolute shrinkage and selection operator (lasso) paths for regularized logistic regression (37) to the training cohort, where the tuning parameter was selected through a 10-fold cross-validation (38). For marker combinations, the sums of selected markers were used to predict tumor status. Notably, although the lasso procedure suggested a linear combination of markers with the weights being the estimated coefficients, the uncertainty associated with these estimated coefficients was large. We therefore used the unweighted sums for all marker combinations for ease of implementation in practice. CIs for AUC were calculated using the DeLong method (39), and for the cut points, the stratified bootstrap percentile method was used. Exact CIs for sensitivity, specificity, and accuracy were obtained on the basis of binomial distributions. All tests were two-sided, and a P value of <0.05 was considered statistically significant. Analyses were performed using R version 3.3.2 and GraphPad Prism 7.


Fig. S1. NPS setup.

Fig. S2. Comparison of PDAC and EV markers on cells and EVs.

Fig. S3. EV markers in a training cohort.

Fig. S4. Comparison of EV counts with conventional clinical metrics.

Fig. S5. NPS signal from validation cohort tEVs.

Fig. S6. EV counts for patients with different types of pancreatic diseases.

Fig. S7. Comparison of the PDACEV signature and SSTR2 in different patient cohorts.

Table S1. Antibodies used in flow cytometry and NPS.

References (40, 41)


Acknowledgments: We thank our clinical colleagues involved in the clinical care of the patients reported here. We also thank A. Roberts for help with screening antibodies before this clinical study. Funding: Part of this study was funded by a grant from the Lustgarten Foundation (R. Weissleder), NIH R01CA204019 (R. Weissleder), P01CA069246 (R. Weissleder), K99CA201248 (H.I.), R01HL113156 (H.L.), R21CA205322 (H.L.), and a pilot grant from the Andrew L. Warshaw, M.D. Institute for Pancreatic Cancer Research at MGH (K.S.Y. and H.I.). C.P. was supported by the CaNCURE program, Northeastern University, NIH (R25CA17174650). Author contributions: K.S.Y., H.I., S.H., and R. Weissleder designed the study and all experiments; K.S.Y., H.I., and S.H. performed all experiments; I.P., C.M.C., and C.F.d.C. collected patient samples; A.F.d.C., S.C., C.-H.H., and C.P. assisted with data collection; K.S.Y., H.I., S.H., R. Wang, R.Y., H.L., and R. Weissleder analyzed the data; S.F. provided new reagents; R. Weissleder, K.S.Y., H.I., C.P., and H.L. provided funding; K.S.Y., H.I., H.L., C.M.C., and R. Weissleder wrote the paper. Competing interests: Exosome Diagnostics Inc. licensed a patent application submitted by MGH that covers the nanoplasmonic sensing system used in the research. H.I., C.M.C., H.L., and R. Weissleder are inventors of the patent application. H.I. and H.L. serve as consultants for Exosome Diagnostics Inc. R. Weissleder is a cofounder of T2 Biosystems and Lumicell. He serves as a scientific advisor for ModeRNA Therapeutics, Tarveda Therapeutics, and Alivio Therapeutics. None of these activities are related to the manuscript. Data and materials availability: Data and materials are available upon request by contacting the corresponding author.
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