Therapeutics, Roche and NeoMed. She receives speaker fees from Roche, owns stock in IDbyDNA, and has an ownership interest in CureMatch, Inc. David Fabrizio and Garett M. Frampton are paid employees of Foundation Medicine, Inc. Paul Riviere discloses consulting fees from Peptide Logic, LLC. Andrew Sharabi reports research funding and honoraria from Pfizer and Varian Medical Systems, consultant fees from Astrazeneca, and other fees from Raysearch and Merck, and is the scientific founder and has an equity interest in Toragen Inc. outside the submitted work. The terms of this arrangement have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies.

Aims/hypothesis Glucagon like peptide-1 (GLP-1) enhances glucose-dependent insulin secretion by binding to GLP-1 receptors (GLP1Rs) on pancreatic beta cells. GLP-1 mimetics are used in the clinic for the treatment of type 2 diabetes, but despite their therapeutic success, several clinical effects of GLP-1 remain unexplained at a mechanistic level, particularly in extrapancreatic tissues. The aim of this study was to generate and characterise a monoclonal antagonistic antibody for the GLP1R for use in vivo. Methods A naive phage display selection strategy was used to isolate single-chain variable fragments (ScFvs) that bound to GLP1R. The ScFv with the highest affinity, Glp1R0017, was converted into a human IgG1 and characterised further. In vitro antagonistic activity was assessed in a number of assays: a cAMP-based homogenous time-resolved fluorescence assay in GLP1R-overexpressing cell lines, a live cell cAMP imaging assay and an insulin secretion assay in INS-1 832/3 cells. Glp1R0017 was further tested in immunostaining of mouse pancreas, and the ability of Glp1R0017 to block GLP1R in vivo was assessed by both IPGTT and OGTT in C57/Bl6 mice.

RAS alterations are often found in difficult-to-treat malignancies and are considered "undruggable." To better understand the clinical correlates and coaltered genes of RAS alterations, we used targeted next-generation sequencing (NGS) to analyze 1,937 patients with diverse cancers. Overall, 20.9% of cancers (405/1,937) harbored RAS alterations. Most RAS-altered cases had genomic coalterations (95.3%, median: 3, range: 0-51), often involving genes implicated in oncogenic signals: PI3K pathway (31.4% of 405 cases), cell cycle (31.1%), tyrosine kinase families (21.5%) and MAPK signaling (18.3%). Patients with RAS-altered versus wild-type RAS malignancies had significantly worse overall survival (OS; p = 0.02 [multivariate]), with KRAS alterations, in particular, showing shorter survival. Moreover, coalterations in both RAS and PI3K signaling or cell-cycle-associated genes correlated with worse OS (p = 0.004 and p < 0.0001, respectively [multivariate]). Among RASaltered patients, MEK inhibitors alone did not impact progression-free survival (PFS), while matched targeted therapy against non-MAPK pathway coalterations alone showed a trend toward longer PFS (vs. patients who received unmatched therapy) (HR: 0.79, 95% CI: 0.61-1.03, p = 0.07). Three of nine patients (33%) given tailored combination therapies targeting both MAPK and non-MAPK pathways achieved objective responses. In conclusion, RAS alterations correlated with poor survival across cancers. The majority of RAS alterations were accompanied by coalterations impacting other oncogenic pathways. MEK inhibitors alone were ineffective against RAS-altered cancers while matched targeted therapy against coalterations alone correlated with a trend toward improved PFS. A subset of the small number of patients given MEK inhibitors plus tailored non-MAPK-targeting agents showed responses, suggesting that customized combinations warrant further investigation. *S.M.L. and R.K. contributed equally to this work Additional Supporting Information may be found in the online version of this article.

Continuous monitoring has the advantage of capturing effects over time that may be missed with manual tests using pre-determined time points. This automated behavioural system does not replace the need for conventional methods but could be implemented simultaneously to improve our understanding of behavioural pharmacology.