Intratumoral Delivery of Chimeric Antigen Receptor T Cells Targeting CD133 Effectively Treats Brain Metastases.

PURPOSE: Brain metastases (BM) are mainly treated palliatively with an expected survival of less than 12 months after diagnosis. In many solid tumors, the human neural stem cell marker glycoprotein CD133 is a marker of a tumor-initiating cell population that contributes to therapy resistance, relapse, and metastasis. EXPERIMENTAL DESIGN: Here, we use a variant of our previously described CD133 binder to generate second-generation CD133-specific chimeric antigen receptor T cells (CAR-T) to demonstrate its specificity and efficacy against multiple patient-derived BM cell lines with variable CD133 antigen expression. RESULTS: Using both lung- and colon-BM patient-derived xenograft models, we show that a CD133-targeting CAR-T cell therapy can evoke significant tumor reduction and survival advantage after a single dose, with complete remission observed in the colon-BM model. CONCLUSIONS: In summary, these data suggest that CD133 plays a critical role in fueling the growth of BM, and immunotherapeutic targeting of this cell population is a feasible strategy to control the outgrowth of BM tumors that are otherwise limited to palliative care. See related commentary by Sloan et al., p. 477.

Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences.

Novel antibiotics are urgently needed to address the looming global crisis of antibiotic resistance. Historically, the primary source of clinically used antibiotics has been microbial secondary metabolism. Microbial genome sequencing has revealed a plethora of uncharacterized natural antibiotics that remain to be discovered. However, the isolation of these molecules is hindered by the challenge of linking sequence information to the chemical structures of the encoded molecules. Here, we present PRISM 4, a comprehensive platform for prediction of the chemical structures of genomically encoded antibiotics, including all classes of bacterial antibiotics currently in clinical use. The accuracy of chemical structure prediction enables the development of machine-learning methods to predict the likely biological activity of encoded molecules. We apply PRISM 4 to chart secondary metabolite biosynthesis in a collection of over 10,000 bacterial genomes from both cultured isolates and metagenomic datasets, revealing thousands of encoded antibiotics. PRISM 4 is freely available as an interactive web application at http://prism.adapsyn.com .