Detection of reliable biomarkers that represent treatment response and drug resistance remains a major clinical challenge for ovarian cancer. Extracellular vesicles (EVs) represent new opportunities as emerging circulating cancer biomarkers. These cell-derived membrane-bound vesicles contain protein and nucleic acid cargos, which could represent a molecular `snapshot' of tumors. Namely, molecular analyses of tumor-derived EVs have shown promise to enable non-invasive, real-time cancer monitoring. A major technical challenge in further exploiting EVs' potential and accelerating their clinical translation is developing a sensitive, robust, and standardized assay that can determine EVs' composition, cellular origins, and molecular profiles in clinical samples. Most EVs are small vesicles with limited numbers of epitopes and surface areas for immunolabeling (i.e., weak detectable signals), which often require sophisticated multi-step signal amplification strategies. A key aspect of new EV assays is thus a robust signal amplification strategy; otherwise, a significant fraction of EVs remain undetected. The overall goal of this application is to address these technical challenges by developing an integrated nanoplasmonic sensing platform. Termed nano-CRISPR, the new system will incorporate single EV capture and analysis using plasmonic nanodisk arrays, CRISPR/Cas13a sensors for lysis-free, amplification-free RNA detection in captured single EVs, and plasmon enhancement for simple, robust signal amplification. Using the integrated system, we will perform rigorous validation experiments using cell lines and patient-derived 3D organoid models with different drug responses, establish a standard operating protocol, and evaluate the assay reproducibility in research and clinical settings. Finally, we will apply the nano- CRISPR technology to comprehensively profile EVs in serially collected human plasma samples of ovarian cancer patients undergoing treatment. Success here will produce an advanced EV detection platform with high sensitivity and multiplexed biomarker sensing capability. This will help the field understand how well EVs' molecular profiles offer additional insight into cancer progress or treatment response and could significantly accelerate the clinical translation of EV analyses as routine procedures for cancer patient care in clinical settings.

The overall goal of this proposal is to further advance the cutting-edge plasmonic EV sensing technology for multiplexed protein and RNA biomarker analysis in single EVs. Using longitudinal clinical samples collected during therapy, we will assess the clinical utilities of EV analyses for monitoring and evaluating treatment response in ovarian cancer.