Fluorescent CSC models evidence that targeted nanomedicines improve treatment sensitivity of breast and colon cancer stem cells.

To be able to study the efficacy of targeted nanomedicines in marginal population of highly aggressive cancer stem cells (CSC), we have developed a novel in vitro fluorescent CSC model that allows us to visualize these cells in heterogeneous population and to monitor CSC biological performance after therapy. In this model tdTomato reporter gene is driven by CSC specific (ALDH1A1) promoter and contrary to other similar models, CSC differentiation and un-differentiation processes are not restrained and longitudinal studies are feasible. We used this model for preclinical validation of poly[(d,l-lactide-co-glycolide)-co-PEG] (PLGA-co-PEG) micelles loaded with paclitaxel. Further, active targeting against CD44 and EGFR receptors was validated in breast and colon cancer cell lines. Accordingly, specific active targeting toward surface receptors enhances the performance of nanomedicines and sensitizes CSC to paclitaxel based chemotherapy. FROM THE CLINICAL EDITOR: Many current cancer therapies fail because of the failure to target cancer stem cells. This surviving population soon proliferates and differentiates into more cancer cells. In this interesting article, the authors designed an in vitro cancer stem cell model to study the effects of active targeting using antibody-labeled micelles containing chemotherapeutic agent. This new model should allow future testing of various drug/carrier platforms before the clinical phase.

Implementation of Zebrafish Ontologies for Toxicology Screening.

Toxicological evaluation of chemicals using early-life stage zebrafish (Danio rerio) involves the observation and recording of altered phenotypes. Substantial variability has been observed among researchers in phenotypes reported from similar studies, as well as a lack of consistent data annotation, indicating a need for both terminological and data harmonization. When examined from a data science perspective, many of these apparent differences can be parsed into the same or similar endpoints whose measurements differ only in time, methodology, or nomenclature. Ontological knowledge structures can be leveraged to integrate diverse data sets across terminologies, scales, and modalities. Building on this premise, the National Toxicology Program's Systematic Evaluation of the Application of Zebrafish in Toxicology undertook a collaborative exercise to evaluate how the application of standardized phenotype terminology improved data consistency. To accomplish this, zebrafish researchers were asked to assess images of zebrafish larvae for morphological malformations in two surveys. In the first survey, researchers were asked to annotate observed malformations using their own terminology. In the second survey, researchers were asked to annotate the images from a list of terms and definitions from the Zebrafish Phenotype Ontology. Analysis of the results suggested that the use of ontology terms increased consistency and decreased ambiguity, but a larger study is needed to confirm. We conclude that utilizing a common data standard will not only reduce the heterogeneity of reported terms but increases agreement and repeatability between different laboratories. Thus, we advocate for the development of a zebrafish phenotype atlas to help laboratories create interoperable, computable data.