Abstract
Abstract The analysis of extracellular vesicles (EVs) in biofluids is an expanding area of biomarker discovery. To be effective, a diagnostic test must differentiate EVs originating from cancer cells versus non-cancerous origins, and also define biomarkers enriched in cancer EVs. Our team has developed a micro-flow cytometry platform using the Apogee A50 to analyze prostate cancer (PCa) EVs in different biofluids. While flow cytometry is powerful tool for quantitative and qualitative evaluation of cellular biomarkers, direct translation of most applications to EVs is challenging. For example, EVs in biofluids typically range in size from 50-1000nm, and are present as low refractive index, polydisperse populations. We have optimized 30 assay parameters for the identification of prostate specific EVs so that disease specific biomarkers may be evaluated. To illustrate the sensitivity of the system, conditioned media from palmitoylated-GFP-LNCaP cells was used as a positive signal sample. When spiked into increasing concentrations of unstained plasma EVs, the GFP spike was detected linearly, even when present as low as 0.03% of the total EV population. Furthermore, the rate of positive signal/uL sample was quantitative after 15-30 seconds of sample analysis. The platform also permits high throughput analysis using a 96 well plate autosampler. To show sample reproducibility, we analyzed both low and high positive-signal samples over an 8hr sampling window. Low positive samples (120events/uL; 0.1% of total EVs) had a 4% CV over 96 samples whereas the high positive signal samples (2300events/uL, 1.0% of total EVs) had a 2.2% CV. Similar CVs have been obtained with CD9 and CD63. Analysis of sample aliquots over multiple days shows less than 6% CV. Conventional flow cytometers acquire tens of thousands of events while the Apogee system is able to collect millions of events with several parameters/event, in a high throughput manner. This generates a significant amount of data for which we have designed a unique machine learning approach to detect specific EV populations within the whole sample. In this manner, we utilize all data from the sample and not simply a subjectively defined area of interest. Using fresh samples from the clinic we have standardized a 2hr arm-freezer protocol for the collection and preservation of serum and plasma. Comparison studies in LNCaps and PC3 cells of 5 antibodies against PSMA, indicated the J591 anti-PSMA monoclonal antibody as a selective marker for prostate cancer EVs. Positive signal for this antibody was linearly titrated in both a LNCaP EV system and plasma EVs from metastatic prostate cancer patients. Specificity was shown by saturating a plasma system with unconjugated J591 and then reacting with PE-conjugated J591. Using the A50 micro-flow cytometry platform, the J591 antibody to identify prostate specific EVs, and a unique machine learning algorithm, we identified a multiple biomarker, diagnostic signature for PCa. Citation Format: Desmond Pink, Robert Paproski, Deborah Sosnowski, Lian Willetts, Eric Hyndman, John D. Lewis. Technical validation of a micro-flow cytometry platform for prostate cancer biomarker discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3799. doi:10.1158/1538-7445.AM2017-3799
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