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

Hinestrosa, J.P., Sears, R.C., Dhani, H. et al. https://doi.org/10.1038/s43856-023-00351-4

Abstract

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.

Case Report: Early detection of pancreatic pre-cancer lesion in multimodal approach with exosome liquid biopsy→

In this case study, the authors utilized the ExoVita Pancreas assay for a patient undergoing acute pancreatitis. The ExoVita Pancreas assay returned a high likelyhood result. Combined with other results, the patient underwent a Whipple procedure, which revealed the presence of a high-grade lesion. 

Harmeet Dhani, Juan Pablo Hinestrosa, Jesus Izaguirre-Carbonell, Heath I Balcer, Razelle Kurzrock, and Paul R Billings doi: 10.3389/fonc.2023.1170513

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ABSTRACT

Background: The detection of pancreatic ductal adenocarcinoma (PDAC) lesions at pre-cancerous or early-stages is critical to improving patient survival. We have developed a liquid biopsy test (ExoVita®) based on the measurement of protein biomarkers in cancer-derived exosomes. The high sensitivity and specificity of the test for early-stage PDAC has the potential to improve a patient’s diagnostic journey in hopes to impact patient outcomes.

Methods: Exosome isolation was performed using alternating current electric (ACE) field applied to the patient plasma sample. Following a wash to eliminate unbound particles, the exosomes were eluted from the cartridge. A downstream multiplex immunoassay was performed to measure proteins of interest on the exosomes, and a proprietary algorithm provided a score for probability of PDAC.

Results: We describe the case of a 60-year-old healthy non-Hispanic white male with acute pancreatitis who underwent numerous invasive diagnostic procedures that failed to detect radiographic evidence of pancreatic lesions. Following the results of our exosome-based liquid biopsy test showing "High Likelihood of PDAC", in addition to KRAS and TP53 mutations, the patient decided to undergo a robotic pancreaticoduodenectomy (Whipple) procedure. Surgical pathology confirmed the diagnosis of high-grade intraductal papillary mucinous neoplasm (IPMN), which was consistent with the results of our ExoVita® test. The patient’s post-operative course was unremarkable. At five-month follow-up, the patient continued to recover well without complications, in addition to a repeat ExoVita test which demonstrated “Low Likelihood of PDAC”.

Conclusion: This case report highlights how a novel liquid biopsy diagnostic test based on the detection of exosome protein biomarkers allowed early diagnosis of a high-grade precancerous lesion for PDAC and improved patient outcome.

cyc‐DEP: Cyclic immunofluorescence profiling of particles collected using dielectrophoresis→

Gustafson KT, Sayar Z, Le H, Gustafson SL, Gower A, Modestino A, Ibsen S, Heller MJ, Esener S, Eksi SE. cyc-DEP: doi: 10.1002/elps.202200001

Abstract

Cancer is a highly heterogenous disease that requires precise detection tools and active surveillance methods. Liquid biopsy assays provide an agnostic way to follow the complex trajectory of cancer, providing better patient stratification tools for optimized treatment. Here, we present the development of a low-volume liquid biopsy assay called cyc-DEP (cyclic immunofluorescent imaging on dielectrophoretic chip) to profile biomarkers collected on a dielectrophoretic microfluidic chip platform. To enable on-chip cyclic imaging, we optimized a fluorophore quenching method and sequential rounds of on-chip staining with fluorescently conjugated primary antibodies. cyc-DEP allows for the quantification of a multiplex array of proteins using 25 µl of a patient plasma sample. We utilized nanoparticles from a prostate adenocarcinoma (LNCaP) cell line and a panel of six target proteins to develop our proof-of-concept technique. We then used cyc-DEP to quantify blood plasma levels of target proteins from healthy individuals, low-grade and high-grade prostate cancer patients (n = 3 each) in order to demonstrate that our platform is suitable for liquid biopsy analysis in its present form. To ensure accurate quantification of signal intensities and comparisons between different samples, we incorporated a signal intensity normalization method (fluorescent beads) and a custom signal intensity quantification algorithm that account for the distribution of signal across hundreds of collection regions on each chip. Our technique enabled a threefold improvement in multiplicity for detecting proteins associated with fluid samples, opening doors for early detection, and active surveillance through quantification of a multiplex array of biomarkers from low-volume liquid biopsies.

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

In this case-controlled pilot study, the authors explored a cohort of 139 pathologically staged patients with stage 1 and stage 2 pancreatic, ovarian, and bladder cancers. Biological Dynamics’ Verita™ proprietary platform detected 96 percent of stage 1 pancreatic cancers and three-quarters of stage 1 ovarian cancers using isolated exosomes and AI-enabled protein marker analysis. 

Juan Pablo Hinestrosa, Razelle Kurzrock, Jean M. Lewis, Nicholas J. Schork, Gregor Schroeder, Ashish M. Kamat, Andrew M. Lowy, Ramez N. Eskander, Orlando Perrera, David Searson, Kiarash Rastegar, Jake R. Hughes, Victor Ortiz, Iryna Clark, Heath I. Balcer, Larry Arakelyan, Robert Turner, Paul R. Billings, Mark J. Adler, Scott M. Lippman, Rajaram Krishnan. doi.org/10.1038/s43856-022-00088-6

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ABSTRACT

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.

Conclusion: 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.

Automated fluorescence quantification of extracellular vesicles collected from blood plasma using dielectrophoresis→

Gustafson KT, Huynh KT, Heineck D, Bueno J, Modestino A, Kim S, Gower A, Armstrong R, Schutt CE, Ibsen SD. doi: 10.1039/d0lc00940g.

Abstract

Tumor-secreted exosomes and other extracellular vesicles (EVs) in circulation contain valuable biomarkers for early cancer detection and screening. We have previously demonstrated collection of cancer-derived nanoparticles (NPs) directly from whole blood and plasma with a chip-based technique that uses a microelectrode array to generate dielectrophoretic (DEP) forces. This technique enables direct recovery of NPs from whole blood and plasma. The biomarker payloads associated with collected particles can be detected and quantified with immunostaining. Accurately separating the fluorescence intensity of stained biomarkers from background (BG) levels becomes a challenge when analyzing the blood from early-stage cancer patients in which biomarker concentrations are low. To address this challenge, we developed two complementary techniques to standardize the quantification of fluorescently immunolabeled biomarkers collected and concentrated at predictable locations within microfluidic chips. The first technique was an automated algorithm for the quantitative analysis of fluorescence intensity at collection regions within the chip compared to levels at adjacent regions. The algorithm used predictable locations of particle collection within the chip geometry to differentiate regions of collection and BG. We successfully automated the identification and removal of optical artifacts from quantitative calculations. We demonstrated that the automated system performs nearly the same as a human user following a standard protocol for manual artifact removal with Pearson's r-values of 0.999 and 0.998 for two different biomarkers (n = 36 patients). We defined a usable dynamic range of fluorescence intensities corresponding to 1 to 2000 arbitrary units (a.u.). Fluorescence intensities within the dynamic range increased linearly with respect to exposure time and particle concentration. The second technique was the implementation of an internal standard to adjust levels of biomarker fluorescence based on the relative collection efficiency of the chip. Use of the internal standard reduced variability in measured biomarker levels due to differences in chip-to-chip collection efficiency, especially at low biomarker concentrations. The internal standard did not affect linear trends between fluorescence intensity and exposure time. Adjustments using the internal standard improved linear trends between fluorescence intensity and particle concentration. The optical quantification techniques described in this paper can be easily adapted for other lab-on-a-chip platforms that have predefined regions of biomarker or particle collection and that rely on fluorescence detection.

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

In this article, the authors used Biological Dynamics technology based on AC Electrokinetics (ACE) for the simultaneous, rapid detection and isolation of cell-free DNA (cfDNA), extracellular vesicle RNA (EV-RNA), and EV-associated protein biomarkers directly from human biofluids.

Juan P. Hinestrosa, David J. Searson, Jean M. Lewis, Alfred Kinana, Orlando Perrera, Irina Dobrovolskaia, Kevin Tran, Robert Turner, Heath I. Balcer, Iryna Clark, David Bodkin, Dave S. Hoon and Rajaram Krishnan. doi.org/10.3389/fbioe.2020.581157.

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ABSTRACT

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.

ARTICLE INFORMATION

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

In this paper, the authors used Biological Dynamics technology to study traumatic brain injury. The work was done in collaboration with Michael Heller's lab at UCSD/OHSU and Dr. Clark Chen, MD, PhD, Head of Neurosurgery at the University of Minnesota Medical School, who would like to continue the work and collaboration due to the exciting results.

Lewis JM, Dhawan S, Obirieze AC, Sarno B, Akers J, Heller MJ, Chen CC. Front. Neurol. 2020 July 14 doi.org/10.3389/fneur.2020.00685

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ABSTRACT

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.

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A Pilot Proof-Of-Principle Analysis Demonstrating Dielectrophoresis (DEP) as a Glioblastoma Biomarker Platform→

In this article, the authors used Biological Dynamic ACE technology to screen plasma samples from brain cancer patients for the presence of both the exosome-associated proteins Tau and GFAP.

Lewis J, Alattar AA, Akers J, Carter BS, Heller MJ, Chen CC. Nature Sci Rep. 2019 Jul 16. doi: 10.1038/s41598-019-46311-8.

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ABSTRACT

Extracellular vesicles (EVs) are small, membrane-bound particles released by all cells that have emerged as an attractive biomarker platform. We study the utility of a dielectrophoretic (DEP) micro-chip device for isolation and characterization of EVs derived from plasma specimens from patients with brain tumors. EVs were isolated by DEP chip and subjected to on-chip immunofluorescence (IF) staining to determine the concentration of glial fibrillary acidic protein (GFAP) and Tau. EVs were analyzed from the plasma samples isolated from independent patient cohorts. Glioblastoma cell lines secrete EVs enriched for GFAP and Tau. These EVs can be efficiently isolated using the DEP platform. Application of DEP to clinical plasma samples afforded discrimination of plasma derived from brain tumor patients relative to those derived from patients without history of brain cancer. Sixty-five percent (11/17) of brain tumor patients showed higher EV-GFAP than the maximum observed in controls. Ninety-four percent (16/17) of tumor patients showed higher EV-Tau than the maximum observed in controls. These discrimination thresholds were applied to plasma isolated from a second, independent cohort of 15 glioblastoma patients and 8 controls. For EV-GFAP, we observed 93% sensitivity, 38% specificity, 74% PPV, 75% NPV, and AUC of 0.65; for EV-Tau, we found 67% sensitivity, 75% specificity 83% PPV, 55% NPV, and AUC of 0.71 for glioblastoma diagnosis. This proof-of-principle study provides support for DEP-IF of plasma EVs for diagnosis of glioblastoma.

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

In this article, the authors used Biological Dynamic ACE technology to screen whole blood and plasma samples from pancreatic cancer patients for the presence of both the exosome-associated protein CD63 and glypican-1 (GPC-1).

 Lewis JM, Vyas AD, Qiu Y, Messer KS, White R, Heller MJ. ACS Nano. 2018 Mar 28. doi: 10.1021/acsnano.7b08199.

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ABSTRACT

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.

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