The role of APOBEC3B in lung tumor evolution and targeted cancer therapy resistance

In this study, the impact of the apolipoprotein B mRNA-editing catalytic subunit-like (APOBEC) enzyme APOBEC3B (A3B) on epidermal growth factor receptor (EGFR)-driven lung cancer was assessed. A3B expression in EGFR mutant (EGFRmut) non-small-cell lung cancer (NSCLC) mouse models constrained tumorigenesis, while A3B expression in tumors treated with EGFR-targeted cancer therapy was associated with treatment resistance. Analyses of human NSCLC models treated with EGFR-targeted therapy showed upregulation of A3B and revealed therapy-induced activation of nuclear factor kappa B (NF-κB) as an inducer of A3B expression. Significantly reduced viability was observed with A3B deficiency, and A3B was required for the enrichment of APOBEC mutation signatures, in targeted therapy-treated human NSCLC preclinical models. Upregulation of A3B was confirmed in patients with NSCLC treated with EGFR-targeted therapy. This study uncovers the multifaceted roles of A3B in NSCLC and identifies A3B as a potential target for more durable responses to targeted cancer therapy.</p

FileWeaver: Gestion Flexible de Fichiers avec Suivi Automatique des Dépendances

International audienceKnowledge management and sharing involves a variety of specialized but isolated software tools, tied together by the files that these tools use and produce. We interviewed 23 scientists and found that they all had difficulties using the file system to keep track of, re-find and maintain consistency among related but distributed information. We introduce FileWeaver, a system that automatically detects dependencies among files without explicit user action, tracks their history, and lets users interact directly with the graphs representing these dependencies and version history. Changes to a file can trigger recipes, either automatically or under user control, to keep the file consistent with its dependants. Users can merge variants of a file, e.g. different output formats, into a polymorphic file, or morph, and automate the management of these variants. By making dependencies among files explicit and visible, FileWeaver facilitates the automation of workflows by scientists and other users who rely on the file system to manage their data

Many Labs 3: Evaluating participant pool quality across the academic semester via replication

The university participant pool is a key resource for behavioral research, and data quality is believed to vary over the course of the academic semester. This crowdsourced project examined time of semester variation in 10 known effects, 10 individual differences, and 3 data quality indicators over the course of the academic semester in 20 participant pools (N = 2696) and with an online sample (N = 737). Weak time of semester effects were observed on data quality indicators, participant sex, and a few individual differences conscientiousness, mood, and stress. However, there was little evidence for time of semester qualifying experimental or correlational effects. The generality of this evidence is unknown because only a subset of the tested effects demonstrated evidence for the original result in the whole sample. Mean characteristics of pool samples change slightly during the semester, but these data suggest that those changes are mostly irrelevant for detecting effects. (C) 2015 Elsevier Inc. All rights reserved

Many Labs 3: Evaluating participant pool quality across the academic semester via replication

The university participant pool is a key resource for behavioral research, and data quality is believed to vary over the course of the academic semester. This crowdsourced project examined time of semester variation in 10 known effects, 10 individual differences, and 3 data quality indicators over the course of the academic semester in 20 participant pools (N = 2696) and with an online sample (N = 737). Weak time of semester effects were observed on data quality indicators, participant sex, and a few individual differences conscientiousness, mood, and stress. However, there was little evidence for time of semester qualifying experimental or correlational effects. The generality of this evidence is unknown because only a subset of the tested effects demonstrated evidence for the original result in the whole sample. Mean characteristics of pool samples change slightly during the semester, but these data suggest that those changes are mostly irrelevant for detecting effects. (C) 2015 Elsevier Inc. All rights reserved