Summary
The transition from castration resistant prostate adenocarcinoma (CRPC) to neuroendocrine prostate cancer (NEPC) has emerged as an important mechanism of treatment resistance. NEPC is associated with over-expression and gene amplification of MYCN (encoding N-Myc). N-Myc is an established oncogene in several rare pediatric tumors, but its role in prostate cancer progression is not well established. Integrating a genetically engineered mouse model and human prostate cancer transcriptome data, we show that N-Myc over-expression leads to the development of poorly differentiated, invasive prostate cancer that is molecularly similar to human NEPC. This includes an abrogation of AR signaling and induction of Polycomb Repressive Complex 2 signaling. Altogether, our data establishes N-Myc as an oncogenic driver of NEPC.
SUMMARY
CRISPR loci are a cluster of repeats separated by short “spacer” sequences derived from prokaryotic viruses and plasmids that determine the targets of the host’s CRISPR-Cas immune response against its invaders. For type I and II CRISPR-Cas systems, single-nucleotide mutations in the seed or protospacer adjacent motif (PAM) of the target sequence cause immune failure and allow viral escape. This is overcome by the acquisition of multiple spacers that target the same invader. Here we show that targeting by the Staphylococcus epidermidis type III-A CRISPR-Cas system does not require PAM or seed sequences, and thus prevents viral escape via single-nucleotide substitutions. Instead, viral escapers can only arise through complete target deletion. Our work shows that, as opposed to type I and II systems, the relaxed specificity of type III CRISPR-Cas targeting provides robust immune responses that can lead to viral extinction with a single spacer targeting an essential phage sequence.
Over the past decade, the rate of drug attrition due to clinical trial failures has risen substantially. Unfortunately it is difficult to identify compounds that have unfavorable toxicity properties before conducting clinical trials. Inspired by the effective use of Sabermetrics in predicting successful baseball players, we sought to use a similar “moneyball” approach that analyzes overlooked features to predict clinical toxicity. We introduce a new data-driven approach (PrOCTOR) that directly predicts the likelihood of toxicity in clinical trials. PrOCTOR integrates properties of a compound’s targets and its structure to provide a new measure, the PrOCTOR score. Drug target network connectivity and expression levels, along with molecular weight, were identified as important indicators of adverse clinical events. Altogether, our method provides a data-driven broadly applicable strategy to identify drugs likely to possess manageable toxicity in clinical trials and will help drive the design of therapeutic agents with less toxicity.
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