Diphtheria toxin (DT) has been utilized as a prospective anti-cancer agent for the targeted delivery of cytotoxic therapy to otherwise untreatable neoplasia. DT is an extremely potent toxin for which the entry of a single molecule into a cell can be lethal. DT has been targeted to cancer cells by deleting the cell receptor-binding domain and combining the remaining catalytic portion with targeting proteins that selectively bind to the surface of cancer cells. It has been assumed that "receptorless" DT cannot bind to and kill cells. In the present study, we report that "receptorless" recombinant DT385 is in fact cytotoxic to a variety of cancer cell lines.
The leaky, heterogeneous vasculature of human tumors prevents the even distribution of systemic drugs within cancer tissues. However, techniques for studying vascular delivery systems in vivo often require complex mammalian models and time-consuming, surgical protocols. The developing chicken embryo is a well-established model for human cancer that is easily accessible for tumor imaging. To assess this model for the in vivo analysis of tumor permeability, human tumors were grown on the chorioallantoic membrane (CAM), a thin vascular membrane which overlays the growing chick embryo. The real-time movement of small fluorescent dextrans through the tumor vasculature and surrounding tissues were used to measure vascular leak within tumor xenografts. Dextran extravasation within tumor sites was selectively enhanced an interleukin-2 (IL-2) peptide fragment or vascular endothelial growth factor (VEGF). VEGF treatment increased vascular leak in the tumor core relative to surrounding normal tissue and increased doxorubicin uptake in human tumor xenografts. This new system easily visualizes vascular permeability changes in vivo and suggests that vascular permeability may be manipulated to improve chemotherapeutic targeting to tumors.
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JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
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In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.