Development of a blood-based extracellular vesicle classifier for detection of early-stage pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has an overall 5-year survival rate of just 12.5% and thus is among the leading causes of cancer deaths. When detected at early stages, PDAC survival rates improve substantially. Testing high-risk patients can increase early-stage cancer detection; however, currently available liquid biopsy approaches lack high sensitivity and may not be easily accessible. METHODS: Extracellular vesicles (EVs) were isolated from blood plasma that was collected from a training set of 650 patients (105 PDAC stages I and II, 545 controls). EV proteins were analyzed using a machine learning approach to determine which were the most informative to develop a classifier for early-stage PDAC. The classifier was tested on a validation cohort of 113 patients (30 PDAC stages I and II, 83 controls). RESULTS: The training set demonstrates an AUC of 0.971 (95% CI = 0.953-0.986) with 93.3% sensitivity (95% CI: 86.9-96.7) at 91.0% specificity (95% CI: 88.3-93.1). The trained classifier is validated using an independent cohort (30 stage I and II cases, 83 controls) and achieves a sensitivity of 90.0% and a specificity of 92.8%. CONCLUSIONS: Liquid biopsy using EVs may provide unique or complementary information that improves early PDAC and other cancer detection. EV protein determinations herein demonstrate that the AC Electrokinetics (ACE) method of EV enrichment provides early-stage detection of cancer distinct from normal or pancreatitis controls.

Pancreatic cancer is one of the deadliest cancers and it is often detected when it is too late, limiting treatment options and reducing survival rates. Identifying blood-based markers of pancreatic cancer may help us to diagnose it earlier, when it is more treatable. Tiny particles circulating in the blood stream, called extracellular vesicles (EVs), contain useful information about tumors, which can be collected with our innovative technology. In this study, we analyzed markers present within EVs and developed a computational tool using this information to identify people with early-stage pancreatic cancer. With further testing in real-world settings, this approach may prove useful for surveillance and early detection of this deadly disease.

Early-stage multi-cancer detection using an extracellular vesicle protein-based blood test.

Background: Detecting cancer at early stages significantly increases patient survival rates. Because lethal solid tumors often produce few symptoms before progressing to advanced, metastatic disease, diagnosis frequently occurs when surgical resection is no longer curative. One promising approach to detect early-stage, curable cancers uses biomarkers present in circulating extracellular vesicles (EVs). To explore the feasibility of this approach, we developed an EV-based blood biomarker classifier from EV protein profiles to detect stages I and II pancreatic, ovarian, and bladder cancer. Methods: Utilizing an alternating current electrokinetics (ACE) platform to purify EVs from plasma, we use multi-marker EV-protein measurements to develop a machine learning algorithm that can discriminate cancer cases from controls. The ACE isolation method requires small sample volumes, and the streamlined process permits integration into high-throughput workflows. Results: In this case-control pilot study, comparison of 139 pathologically confirmed stage I and II cancer cases representing pancreatic, ovarian, or bladder patients against 184 control subjects yields an area under the curve (AUC) of 0.95 (95% CI: 0.92 to 0.97), with sensitivity of 71.2% (95% CI: 63.2 to 78.1) at 99.5% (97.0 to 99.9) specificity. Sensitivity is similar at both early stages [stage I: 70.5% (60.2 to 79.0) and stage II: 72.5% (59.1 to 82.9)]. Detection of stage I cancer reaches 95.5% in pancreatic, 74.4% in ovarian (73.1% in Stage IA) and 43.8% in bladder cancer. Conclusions: This work demonstrates that an EV-based, multi-cancer test has potential clinical value for early cancer detection and warrants future expanded studies involving prospective cohorts with multi-year follow-up.

Simultaneous Isolation of Circulating Nucleic Acids and EV-Associated Protein Biomarkers From Unprocessed Plasma Using an AC Electrokinetics-Based Platform.

The power of personalized medicine is based on a deep understanding of cellular and molecular processes underlying disease pathogenesis. Accurately characterizing and analyzing connections between these processes is dependent on our ability to access multiple classes of biomarkers (DNA, RNA, and proteins)-ideally, in a minimally processed state. Here, we characterize a biomarker isolation platform that enables simultaneous isolation and on-chip detection of cell-free DNA (cfDNA), extracellular vesicle RNA (EV-RNA), and EV-associated proteins in unprocessed biological fluids using AC Electrokinetics (ACE). Human biofluid samples were flowed over the ACE microelectrode array (ACE chip) on the Verita platform while an electrical signal was applied, inducing a field that reversibly captured biomarkers onto the microelectrode array. Isolated cfDNA, EV-RNA, and EV-associated proteins were visualized directly on the chip using DNA and RNA specific dyes or antigen-specific, directly conjugated antibodies (CD63, TSG101, PD-L1, GPC-1), respectively. Isolated material was also eluted off the chip and analyzed downstream by multiple methods, including PCR, RT-PCR, next-generation sequencing (NGS), capillary electrophoresis, and nanoparticle size characterization. The detection workflow confirmed the capture of cfDNA, EV-RNA, and EV-associated proteins from human biofluids on the ACE chip. Tumor specific variants and the mRNAs of housekeeping gene PGK1 were detected in cfDNA and RNA isolated directly from chips in PCR, NGS, and RT-PCR assays, demonstrating that high-quality material can be isolated from donor samples using the isolation workflow. Detection of the luminal membrane protein TSG101 with antibodies depended on membrane permeabilization, consistent with the presence of vesicles on the chip. Protein, morphological, and size characterization revealed that these vesicles had the characteristics of EVs. The results demonstrated that unprocessed cfDNA, EV-RNA, and EV-associated proteins can be isolated and simultaneously fluorescently analyzed on the ACE chip. The compatibility with established downstream technologies may also allow the use of the platform as a sample preparation method for workflows that could benefit from access to unprocessed exosomal, genomic, and proteomic biomarkers.

Plasma Biomarker for Post-concussive Syndrome: A Pilot Study Using an Alternating Current Electro-Kinetic Platform.

Background: Technology platforms that afford biomarker discovery in patients suffering from traumatic brain injury (TBI) remain an unmet medical need. Here, we describe an observational pilot study to explore the utility of an alternating current electrokinetic (ACE) microchip device in this context. Methods: Blood samples were collected from participating subjects with and without minor TBI. Plasma levels of glial fibrillary acidic protein (GFAP), Tau, ubiquitin C-terminal hydrolase L1 (UCH-L1), and cell-free DNA (cfDNA) were determined in subjects with and without minor TBI using ACE microchip device followed by on-chip immunofluorescent analysis. Post-concussive symptoms were assessed using the Rivermead Post Concussion Symptoms Questionnaire (RPCSQ) at one-month follow-up. Results: Highest levels of GFAP, UCH-L1, and Tau were seen in two minor TBI subjects with abnormality on head computed tomography (CT). In patients without abnormal head CT, Tau and GFAP levels discriminated between plasma from minor-TBI and non-TBI patients, with sensitivity and specificity of 64-72 and 50%, respectively. Plasma GFAP, UCH-L1, and Tau strongly correlated with the cumulative RPCSQ score. Plasma UCH-L1 and GFAP exhibited highest correlation to sensitivity to noise and light (r = 0.96 and 0.91, respectively, p < 0.001). Plasma UCH-L1 and Tau showed highest correlation with headache (r = 0.74 and 0.78, respectively, p < 0.001), sleep disturbance (r = 0.69 and 0.84, respectively, p < 0.001), and cognitive symptoms, including forgetfulness (r = 0.76 and 0.74, respectively, p < 0.001), poor concentration (r = 0.68 and 0.76, respectively, p < 0.001), and time required for information processing (r = 0.77 and 0.81, respectively, p < 0.001). cfDNA exhibited a strong correlation with depression (r = 0.79, p < 0.01) and dizziness (r = 0.69, p < 0.01). While cfDNA demonstrated positive correlation with dizziness and depression (r = 0.69 and 0.79, respectively, p < 0.001), no significant correlation was observed between cumulative RPCSQ and cfDNA (r = 0.07, p = 0.81). Conclusion: We provide proof-of-principle results supporting the utility of ACE microchip for plasma biomarker analysis in patients with minor TBI.

Integrated Analysis of Exosomal Protein Biomarkers on Alternating Current Electrokinetic Chips Enables Rapid Detection of Pancreatic Cancer in Patient Blood.

Pancreatic ductal adenocarcinoma (PDAC) typically has nonspecific symptoms and is often found too late to treat. Because diagnosis of PDAC involves complex, invasive, and expensive procedures, screening populations at increased risk will depend on developing rapid, sensitive, specific, and cost-effective tests. Exosomes, which are nanoscale vesicles shed into blood from tumors, have come into focus as valuable entities for noninvasive liquid biopsy diagnostics. However, rapid capture and analysis of exosomes with their protein and other biomarkers have proven difficult. Here, we present a simple method integrating capture and analysis of exosomes and other extracellular vesicles directly from whole blood, plasma, or serum onto an AC electrokinetic microarray chip. In this process, no pretreatment or dilution of sample is required, nor is it necessary to use capture antibodies or other affinity techniques. Subsequent on-chip immunofluorescence analysis permits specific identification and quantification of target biomarkers within as little as 30 min total time. In this initial validation study, the biomarkers glypican-1 and CD63 were found to reflect the presence of PDAC and thus were used to develop a bivariate model for detecting PDAC. Twenty PDAC patient samples could be distinguished from 11 healthy subjects with 99% sensitivity and 82% specificity. In a smaller group of colon cancer patient samples, elevated glypican-1 was observed for metastatic but not for nonmetastatic disease. The speed and simplicity of ACE exosome capture and on-chip biomarker detection, combined with the ability to use whole blood, will enable seamless "sample-to-answer" liquid biopsy screening and improve early stage cancer diagnostics.

Rapid Isolation and Detection of Exosomes and Associated Biomarkers from Plasma.

Exosomes found in the circulation are a primary source of important cancer-related RNA and protein biomarkers that are expected to lead to early detection, liquid biopsy, and point-of-care diagnostic applications. Unfortunately, due to their small size (50-150 nm) and low density, exosomes are extremely difficult to isolate from plasma. Current isolation methods are time-consuming multistep procedures that are unlikely to translate into diagnostic applications. To address this issue, we demonstrate the ability of an alternating current electrokinetic (ACE) microarray chip device to rapidly isolate and recover glioblastoma exosomes from undiluted human plasma samples. The ACE device requires a small plasma sample (30-50 µL) and is able to concentrate the exosomes into high-field regions around the ACE microelectrodes within 15 min. A simple buffer wash removes bulk plasma materials, leaving the exosomes concentrated on the microelectrodes. The entire isolation process and on-chip fluorescence analysis is completed in less than 30 min which enables subsequent on-chip immunofluorescence detection of exosomal proteins, and provides viable mRNA for RT-PCR analysis. These results demonstrate the ability of the ACE device to streamline the process for isolation and recovery of exosomes, significantly reducing the number of processing steps and time required.

Detecting cancer biomarkers in blood: challenges for new molecular diagnostic and point-of-care tests using cell-free nucleic acids.

As we move into the era of individualized cancer treatment, the need for more sophisticated cancer diagnostics has emerged. Cell-free (cf) nucleic acids (cf-DNA, cf-RNA) and other cellular nanoparticulates are now considered important and selective biomarkers. There is great hope that blood-borne cf-nucleic acids can be used for 'liquid biopsies', replacing more invasive tissue biopsies to analyze cancer mutations and monitor therapy. Conventional techniques for cf-nucleic acid biomarker isolation from blood are generally time-consuming, complicated and expensive. They require relatively large blood samples, which must be processed to serum or plasma before isolation of biomarkers can proceed. Such cumbersome sample preparation also limits the widespread use of powerful, downstream genomic analyses, including PCR and DNA sequencing. These limitations also preclude rapid, point-of-care diagnostic applications. Thus, new technologies that allow rapid isolation of biomarkers directly from blood will permit seamless sample-to-answer solutions that enable next-generation point-of-care molecular diagnostics.

Librarians collaborating with nurses.

Nurses have the potential to be one of the largest groups of users a medical library can serve. In recognition of this fact, librarians at St. John's Health System embarked on a journey to collaborate with nurses and increase this group's use of the medical library. In 2004, a nursing outreach plan was developed by librarians, with input from non-physician health care employees. The nursing outreach plan will be reviewed along with the barriers and successes encountered during implementation of the plan. Innovative strategies for reaching nursing personnel were accomplished by partnering with nurses to reach nurses.