Maximizing the Efficacy of MAPK-Targeted Treatment in PTENLOF/BRAFMUT Melanoma through PI3K and IGF1R Inhibition

The introduction of MAPK pathway inhibitors paved the road for significant advancements in the treatment of BRAF-mutant (BRAF(MUT)) melanoma. However, even BRAF/MEK inhibitor combination therapy has failed to offer a curative treatment option, most likely because these pathways constitute a codependent signaling network. Concomitant PTEN loss of function (PTEN(LOF)) occurs in approximately 40% of BRAF(MUT) melanomas. In this study, we sought to identify the nodes of the PTEN/PI3K pathway that would be amenable to combined therapy with MAPK pathway inhibitors for the treatment of PTEN(LOF)/BRAF(MUT) melanoma. Large-scale compound sensitivity profiling revealed that PTEN(LOF) melanoma cell lines were sensitive to PI3Kβ inhibitors, albeit only partially. An unbiased shRNA screen (7,500 genes and 20 shRNAs/genes) across 11 cell lines in the presence of a PI3Kβ inhibitor identified an adaptive response involving the IGF1R-PI3Kα axis. Combined inhibition of the MAPK pathway, PI3Kβ, and PI3Kα or insulin-like growth factor receptor 1 (IGF1R) synergistically sustained pathway blockade, induced apoptosis, and inhibited tumor growth in PTEN(LOF)/BRAF(MUT) melanoma models. Notably, combined treatment with the IGF1R inhibitor, but not the PI3Kα inhibitor, failed to elevate glucose or insulin signaling. Taken together, our findings provide a strong rationale for testing combinations of panPI3K, PI3Kβ + IGF1R, and MAPK pathway inhibitors in PTEN(LOF)/BRAF(MUT) melanoma patients to achieve maximal response

Process Calculi Abstractions for Biology

Several approaches have been proposed to model biological systems by means of the formal techniques and tools available in computer science. To mention just a few of them, some representations are inspired by Petri nets theory and others by stochastic processes. A most recent approach consists in interpreting living entities as terms of process calculi, by composition of a few behavioural abstractions. This paper comparatively surveys the state of the art of the process calculi approach to biological modelling. The modelling features of a set of calculi are tested against a simple biological scenario, and available extensions and tools are briefly commented upon