Identifying drivers of breast cancer metastasis in progressively invasive subpopulations of zebrafish-xenografted MDA-MB-231.

Cancer metastasis is the primary cause of the high mortality rate among human cancers. Efforts to identify therapeutic agents targeting cancer metastasis frequently fail to demonstrate efficacy in clinical trials despite strong preclinical evidence. Until recently, most preclinical studies used mouse models to evaluate anti-metastatic agents.

Author Correction: Prioritizing multiple therapeutic targets in parallel using automated DNA-encoded library screening.

This corrects the article DOI: 10.1038/ncomms16081.

A multiplex preclinical model for adenoid cystic carcinoma of the salivary gland identifies regorafenib as a potential therapeutic drug.

Adenoid cystic carcinomas (ACC) are rare salivary gland cancers with a high incidence of metastases. In order to study this tumor type, a reliable model system exhibiting the molecular features of this tumor is critical, but none exists, thereby inhibiting in-vitro studies and the analysis of metastatic behavior. To address this deficiency, we have coupled an efficient method to establish tumor cell cultures, conditional reprogramming (CR), with a rapid, reproducible and robust in-vivo zebrafish model.

Prioritizing multiple therapeutic targets in parallel using automated DNA-encoded library screening.

The identification and prioritization of chemically tractable therapeutic targets is a significant challenge in the discovery of new medicines. We have developed a novel method that rapidly screens multiple proteins in parallel using DNA-encoded library technology (ELT). Initial efforts were focused on the efficient discovery of antibacterial leads against 119 targets from Acinetobacter baumannii and Staphylococcus aureus.