STAT3 regulated ARF expression suppresses prostate cancer metastasis

Prostate cancer (PCa) is the most prevalent cancer in men. Hyperactive STAT3 is thought to be oncogenic in PCa. However, targeting of the IL-6/STAT3 axis in PCa patients has failed to provide therapeutic benefit. Here we show that genetic inactivation of Stat3 or IL-6 signalling in a Pten-deficient PCa mouse model accelerates cancer progression leading to metastasis. Mechanistically, we identify p19(ARF) as a direct Stat3 target. Loss of Stat3 signalling disrupts the ARF-Mdm2-p53 tumour suppressor axis bypassing senescence. Strikingly, we also identify STAT3 and CDKN2A mutations in primary human PCa. STAT3 and CDKN2A deletions co-occurred with high frequency in PCa metastases. In accordance, loss of STAT3 and p14(ARF) expression in patient tumours correlates with increased risk of disease recurrence and metastatic PCa. Thus, STAT3 and ARF may be prognostic markers to stratify high from low risk PCa patients. Our findings challenge the current discussion on therapeutic benefit or risk of IL-6/STAT3 inhibition.

A Molecular Mechanism for Sequential Activation of a G Protein-Coupled Receptor

Ligands targeting G protein-coupled receptors (GPCRs) are currently classified as either orthosteric, allosteric, or dualsteric/bitopic. Here, we introduce a new pharmacological concept for GPCR functional modulation: sequential receptor activation. A hallmark feature of this is a stepwise ligand binding mode with transient activation of a first receptor site followed by sustained activation of a second topographically distinct site. We identify 4-CMTB (2-(4-chlorophenyl)-3-methyl-N-(thiazol-2-yl)butanamide), previously classified as a pure allosteric agonist of the free fatty acid receptor 2, as the first sequential activator and corroborate its two-step activation in living cells by tracking integrated responses with innovative label-free biosensors that visualize multiple signaling inputs in real time. We validate this unique pharmacology with traditional cellular readouts, including mutational and pharmacological perturbations along with computational methods, and propose a kinetic model applicable to the analysis of sequential receptor activation. We envision this form of dynamic agonism as a common principle of nature to spatiotemporally encode cellular information.