Program Official

Principal Investigator

Hakho
Lee
Awardee Organization

Massachusetts General Hospital
United States

Fiscal Year
2022
Activity Code
U01
Early Stage Investigator Grants (ESI)
Not Applicable
Project End Date

Early Detection through Novel OCEAN Technology - Ovarian Cancer Exosomal Analysis with Nanoplasmonics

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 reflect 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 workflows. 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 μL). 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 profile 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 scientific outcomes of this research could have a significant translational impact in cancer management, establishing a robust, highly specific liquid biopsy for early detection of ovarian cancer.
  • Batool SM, Yekula A, Khanna P, Hsia T, Gamblin AS, Ekanayake E, Escobedo AK, You DG, Castro CM, Im H, Kilic T, Garlin MA, Skog J, Dinulescu DM, Dudley J, Agrawal N, Cheng J, Abtin F, Aberle DR, Chia D, Elashoff D, Grognan T, Krysan K, Oh SS, Strom C, Tu M, Wei F, Xian RR, Skates SJ, Zhang DY, Trinh T, Watson M, Aft R, Rawal S, Agarwal A, Kesmodel SB, Yang C, Shen C, Hochberg FH, Wong DTW, Patel AA, Papadopoulos N, Bettegowda C, Cote RJ, Srivastava S, Lee H, Carter BS, Balaj L. The Liquid Biopsy Consortium: Challenges and opportunities for early cancer detection and monitoring. Cell reports. Medicine. 2023 Oct 17;4(10):101198. Epub 2023 Sep 15. PMID: 37716353
  • Landeros C, Oh J, Weissleder R, Lee H. Deep learning pipeline for automated cell profiling from cyclic imaging. Scientific reports. 2024 Oct 9;14(1):23600. PMID: 39384907
  • Yang KS, Lin HY, Curley C, Welch MW, Wolpin BM, Lee H, Weissleder R, Im H, Castro CM. Bead-Based Extracellular Vesicle Analysis Using Flow Cytometry. Advanced biosystems. 2020 Dec;4(12):e2000203. Epub 2020 Oct 25. PMID: 33103361