Extracellular vesicles (EVs) are a new class of circulating biomarker that promises non-invasive, real-time cancer monitoring. Many studies have shown that i) EVs may function as reliable surrogates of parental cancer cells, and ii) these vesicles can re?ect global tumor burden, overcoming limitations of tumor heterogeneity and sampling bias. Translating EV analyses into a clinically relevant cancer test, however, is limited by the lack of standardized, practical methods. Conventional assay tools (e.g., ultracentrifugation, Western blotting, ELISA) require large amounts of EVs and extensive processing, making them impractical for clinical work?ows. Moreover, variations in sample handling and testing protocols often lead to inconsistent and confounding results. The goal of this proposal is to i) address such technical challenges in EV analyses, and ii) rigorously evaluate EVs' clinical value as a novel biomarker for early detection of ovarian cancer. 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 the Massachusetts General Hospital, a pioneer in developing novel analytical technologies for EV analyses. These teams will bring their multidisciplinary expertise, innovative technologies and complementary resources to carry out the following translational projects. First, we will advance a standardized EV assay platform. We will adopt our recently developed ExoLution platform to streamline EV collection, and the nPLEX (nano-plasmonic exosome) technology for high-throughput EV protein screening. Our initial study showed that nPLEX achieved >1000-fold higher sensitivity than conventional methods and yet consumed scant sample volumes (0.1 ?L). We now seek to advance nPLEX to the instrument level by i) improving its robustness and throughput, and ii) establishing 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 EVs can be exploited as a biomarker for early detection of ovarian cancer and progression monitoring. Our preclinical study will use patient-derived ovarian cancer cells and novel genetically engineered mouse models to identify EV protein signatures for ovarian cancer. We will next pro?le circulating EVs from ovarian cancer patients and assess the correlation between tumor burden and EV protein signature. Our study will be designed to ensure assay reliability and reproducibility, thereby delivering clinically translatable EV diagnostics. We will impose stringent quality controls 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 management, establishing a robust, highly speci?c liquid biopsy for early detection of ovarian cancer.
We propose to translate a novel liquid biopsy platform for early cancer detection. Specifically, we will advance a forward-thinking nano-plasmonic platform for comprehensive, high-throughput molecular analyses of extracellular vesicles (EVs). As a targeted cancer application of the platform, we will rigorously address EVs' clinical utility for early detection 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 |
