Extracellular vesicles (EVs) have emerged as a promising surrogate for the tissue biopsy, potentially enabling non-invasive, real-time cancer monitoring. Most cancer cells release large numbers of EVs into circulation that carry molecular constituents of the parent tumor. Analyzing EVs could thus offer new avenues to assess tumor burden and tumor heterogeneity. Adoption of EV analyses into a clinical work?ow, however, is impeded by the lack of standardized, practical methods. Conventional assay tools (e.g., ultracentrifugation, Western blotting, ELISA) require large sample volumes and extensive processing; they are impractical for clinical applications. Variations in sample handling and testing protocols often lead to inconsistent and variable ?ndings. The goal of this proposal is i) to address such technical challenges by advancing a robust platform for EV protein analyses, and ii) to rigorously evaluate EVs' clinical value as cancer biomarkers. We formed a strategic academic-industry partnership to achieve this goal: Exosome Diagnostics, an industry leader in EV-based cancer diagnostics, offering ready capacity to develop and manufacture in-vitro diagnostic medical devices; and the Center for Systems Biology at Massachusetts General Hospital, a pioneer in developing novel analytical technologies for EV analyses. These teams will bring in their multidisciplinary expertise, innovative technologies and complementary resources to carry out the following translational projects: First, we will build a sensitive, high-throughput platform for EV protein screening. The system will adopt our recently developed nPLEX (nano-plasmonic exosome) technology that is based on novel surface plasmon resonance (SPR) through nanohole structures. Our initial study showed that nPLEX achieved >1000-fold higher sensitivity than conventional methods and yet consumed scant sample volumes (0.1 ?L). The new nPLEX system will have expanded analytical capacity and scalability for commercial production. We will design a new SPR chip and a detection instrument for massively parallel EV screening, and also establish standardized assay protocols. Leveraging the developmental and regulatory expertise of Exosome Diagnostics, the resulting platform will be ready for translation into clinical diagnostic laboratories. Second, we will perform a targeted clinical study, particularly testing whether EV protein signatures can be used as a biomarker for cancer detection and treatment monitoring. We will collect circulating EVs from ovarian cancer patients undergoing therapies, and track serial changes of EV protein levels. We will ensure assay reliability and reproducibility to deliver clinically translatable EV diagnostics. We will follow stringent quality control on device design and sample processing, accrue well-annotated patient and control samples, and perform multisite testing. The technical and scienti?c outcomes of this research could have a signi?cant translational impact in cancer, establishing a robust, highly speci?c assay to guide treatment decision and assess therapeutic ef?cacy. Exosome Diagnostics will provide regulatory guidance as well as channels to market new tests and devices.
We propose to translate a novel diagnostic technology for circulating biomarkers. Specifically, we will implement a forward-thinking nano-plasmonic system for comprehensive, high-throughput molecular analyses of extracellular vesicles (EVs). As a targeted cancer application, the patient-oriented study will rigorously address EVs' clinical utility for diagnosis, profiling, and treatment monitoring of ovarian cancer.
| Kim, Sung-Jin; Wang, Chuangqi; Zhao, Bing et al. (2018) Deep transfer learning-based hologram classification for molecular diagnostics. Sci Rep 8:17003 |
