Engineered cytomembrane nanovesicles trigger in situ storm of engineered extracellular vesicles for cascade tumor penetration and immune microenvironment remodeling
Source abstract
Abstract
Immunotherapy for triple-negative breast cancer (TNBC) is hindered by its immunologically "cold" microenvironment, reducing treatment efficacy. Converting cold tumors into hot ones can significantly enhance the efficacy of immunotherapy. Nevertheless, the tumor extracellular matrix-associated physical barriers impede the infiltration of immunomodulating agents . Our previous research suggested that extracellular vesicles (EVs) secreted in situ by tumor cells could mediate the intercellular transport and deep infiltration of antitumor drugs . However, EVs’ application faces obstacles such as inefficient delivery, lack of specificity, low production yields, and manufacturing inconsistencies. Based on this, we explored artificial cytomembrane nanovesicles (NVs) as the biomimetics of EVs. Herein, NVs are genetically engineered with membrane fusion-promoting protein VSVG to form VSVG-NVs (V-NVs). The calcium ionophore A23187 and plasmid encoding TNF-α with Lamp2b are loaded into V-NVs (V-NVs/T+A) using electroporation technology. The fusogenic VSVG component facilitates the integration of NVs with the target cancer cells and further incorporation into secreted EVs. The A23187 enhances EVs secretion by increasing intracellular calcium level. This synergistic approach ensures efficient intracellular delivery of TNF-α through EVs, facilitating deep tumor infiltration and remodeling of the immune microenvironment. As expected, the EV-hitchhiking strategy for cascade tumor deep penetration and immune microenvironment modulation demonstrates significant potential to enhance cancer immunotherapy .
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Extracellular vesicles in disease · MicroRNA in disease regulation · Nanoplatforms for cancer theranostics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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