Extracellular vesicles (EVs) are lipid bilayer-enclosed particles that are released from cells. EVs may contain proteins derived from their cells of origin with the potential as diagnostic biomarkers indicating the state of the cells when released. However, due to their small size (50-1000nm), the methods currently used to phenotype EVs have limited sensitivity and scale. A need exists for development of novel technologies improving EV detection and phenotyping.
National Cancer Institute (NCI) scientists have developed a software package to perform high-throughput multi-dimensional analysis of EVs. The software utilizes a multiplex bead-based approach, coupled with secondary markers, clinical data, and -omics data. This technology provides a mechanism for high-throughput, semi-automated multidimensional data analysis for potential diagnostic and prognostic outcomes. The inventors used the software to identify and visualize a broad range of EV subsets, while also indirectly measuring specific EV populations. Exploratory studies confirmed strong correlations of liquid biopsy EV repertoires with tumor burden and responses to treatment. Furthermore, this software allows a scalable method of using EVs as biomarkers in a highly multiplexed fashion. When coupled with other clinical data, it is a useful means of diagnostic and/or prognostic outcomes.
The NCI seeks research co-development partners and/or licensees for a biomarker analysis software for high-throughput diagnostic multiplex data.