Edible plants have been widely used in traditional therapeutics because of the biological activities of their natural ingredients, including anticancer, antioxidant, and anti-inflammatory properties. Plant sap contains such medicinal substances and their secondary metabolites provide unique chemical structures that contribute to their therapeutic efficacy. Plant extracts are known to contain a variety of extracellular vesicles (EVs) but the effects of such EVs on various cancers have not been investigated. Here, we extracted EVs from four plants—Dendropanax morbifera, Pinus densiflora, Thuja occidentalis, and Chamaecyparis obtusa—that are known to have cytotoxic effects. We evaluated the cytotoxic effects of these EVs by assessing their ability to selectively reduce the viability of various tumor cell types compared with normal cells and low metastatic cells. EVs from D. morbifera and P. densiflora sap showed strong cytotoxic effects on tumor cells, whereas those from T. occidentalis and C. obtusa had no significant effect on any tumor cell types. We also identified synergistic effect of EVs from D. morbifera and P. densiflora saps on breast and skin tumor cells and established optimized treatment concentrations. Our findings suggest these EVs from plant sap as new candidates for cancer treatment.

Natural medicinal plants have attracted considerable research attention for their potential as effective drugs. The roots, leaves and stems of the plant, Dendropanax morbifera, which is endemic to southern regions of Asia, have long been used as a folk medicine to treat variety of diseases. However, the sap of this plant has not been widely studied and its bioactive properties have yet to be clearly elucidated. Here, we isolated extracellular vesicles from D. morbifera sap with the goal of improving the intracellular delivery efficiency and clinical effectiveness of bioactive compounds in D. morbifera sap. We further investigated the anti-metastatic effects of D. morbifera sap-derived extracellular vesicles (DMS-EVs) using a cancer metastasis model based on 3D microfluidic system that closely mimics the in vivo tumor environment. We found that DMS-EVs exerted a concentration-dependent suppressive effect on cancer-associated fibroblasts (CAFs), which are important mediators of cancer metastasis. DMS-EVs also altered expression level of genes, especially growth factor and extracellular matrix (ECM)-related genes, including integrin and collagen. Our findings suggest that DMS-EVs can act as anti-CAF agents to reduce CAFs in the tumor microenvironment. They further indicate the utility of our 3D microfluidic model for various drug-screening assays as a potential alternative to animal testing for use in validating therapeutic effects on cancer metastasis.

Cancer-associated fibroblasts (CAFs) in the cancer microenvironment play an essential role in metastasis. Differentiation of endothelial cells into CAFs is induced by cancer cell-derived exosomes secreted from cancer cells that transfer molecular signals to surrounding cells. Differentiated CAFs facilitate migration of cancer cells to different regions through promoting extracellular matrix (ECM) modifications. However, in vitro models in which endothelial cells exposed to cancer cell-derived exosomes secreted from various cancer cell types differentiate into CAFs or a microenvironmentally controlled model for investigating cancer cell invasion by CAFs have not yet been studied. In this study, we propose a three-dimensional in vitro cancer cell invasion model for real-time monitoring of the process of forming a cancer invasion site through CAFs induced by exosomes isolated from three types of cancer cell lines. The invasiveness of cancer cells with CAFs induced by cancer cell-derived exosomes (eCAFs) was significantly higher than that of CAFs induced by cancer cells (cCAFs) through physiological and genetic manner. In addition, different genetic tendencies of the invasion process were observed in the process of invading cancer cells according to CAFs. Our 3D microfluidic platform helps to identify specific interactions among multiple factors within the cancer microenvironment and provides a model for cancer drug development.