Multiplex Detection of Tumor-derived EVs for Monitoring Drug Resistance in Ovarian Cancer

In high-grade serous ovarian carcinoma (HGSOC) patients, recurrence after treatment is very common, with the majority occurring within three years, and the failure is mainly due to treatment resistance. Even for the blockbuster drug Antibody—Drug-Conjugate (ADC), growing resistance in tumors is a significant hurdle. Thus, it is critical to identify patients with acquired resistance and pivot the treatment strategy to prolong overall survival.
Molecular analysis of liquid biopsies could provide new opportunities to closely monitor cancer patients with treatment and predict the efficacy earlier than currently possible. Among several circulating biomarkers, molecular analysis of extracellular vesicles (EVs) could be an attractive approach to monitor ADC resistance, as EVs carry proteins and genetic materials reflective of originating tumors. We hypothesized that molecular analysis of tumor-derived EVs reflects the molecular status of originating tumors and their resistance to ADC therapy. Namely, our nanoplasmonic EV sensing platform (nPLEX) detected multiple EV biomarkers in a single vesicle level to accurately identify tumor-derived EVs and changes in target antigens and other drug-resistant biomarkers within tumor-derived EVs.
In this project, we developed multiplexed single EV analysis technology for predicting and detecting resistance to ADC treatment. Specifically, we aimed to develop an advanced single EV assay based on the nPLEX technology to monitor changes in ADC-related biomarkers in tumor-derived EVs and apply the nanotechnology to monitor patients under ADC clinical trials and evaluate the technology’s accuracy in identifying patients developing resistance to ADC.
This technology would be the clinically translatable ADC-resistance prediction assay, offering a new method to closely monitor tumor response to treatment. This will provide a new opportunity to guide patients to better and more effective treatment while minimizing delays and side effects.