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Chia, B.

Publications and source records attributed to Chia, B..

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AMPKi overcomes the paradoxical activation of CRAF driven by RAF inhibitors through blocking the 14-3-3 binding to its carboxyl-terminus

The paradoxical activation of RAF kinase is the predominant challenge in cancer therapies with RAF inhibitors. The inhibitor-bound RAF molecules are able to transactivate their wild-type binding partners. 14-3-3 that binds to the carboxyl-terminus of RAF kinase has been suggested to regulate the dimer-dependent activation of RAF kinase under physiological conditions, though the molecular basis is not clear. In this study, we investigated the role of 14-3-3 in the paradoxical effect of RAF inhibitors. Firstly, we found that the 14-3-3 binding to the carboxyl-terminus of CRAF was essential for its transactivation. Further, we demonstrated that this binding enhanced the dimer affinity of CRAF. Since 14-3-3 binds to the phosphorylated motif, we next investigated and identified AMPK and CRAF itself as two putative kinases that phosphorylate redundantly the 14-3-3 binding motif of CRAF. Among RAF isoforms, CRAF plays a dominant role in the paradoxical effect of RAF inhibitors, and we thus determined whether the combinatory inhibition of AMPK and CRAF would block this effect. Indeed, our data showed that AMPKi not only blocked the RAF inhibitor-driven paradoxical activation of RAF signaling and cellular overgrowth in Ras-mutated cancer cells but also reduced the drug-resistant clones derived from BRAF(V600E)-mutated cancer cells. Finally, we showed that the 14-3-3 binding to the carboxyl-terminus of CRAF was dispensable for its catalytic function in vivo. Together, our study unraveled how 14-3-3 regulates the dimerization-driven RAF activation and identified AMPKi as a potential method to relieve the drug resistance and side effect of RAF inhibitors in cancer therapy.

cancer biology

ConsensusDriver Improves Upon Individual Algorithms For Predicting Driver Alterations In Different Cancer Types And Individual Patients -- A Toolbox For Precision Oncology

BackgroundIn recent years, several large-scale cancer genomics studies have helped generate detailed molecular profiling datasets for many cancer types and thousands of patients. These datasets provide a unique resource for studying cancer driver prediction methods and their utility for precision oncology, both to predict driver genetic alterations in patient subgroups (e.g. defined by histology or clinical phenotype) or even individual patients.\n\nMethodsWe performed the most comprehensive assessment to date of 18 driver gene prediction methods, on more than 3,400 tumour samples, from 15 cancer types, to determine their suitability in guiding precision medicine efforts. These methods have diverse approaches, which can be classified into five categories: functional impact on proteins in general (FI) or specific to cancer (FIC), cohort-based analysis for recurrent mutations (CBA), mutations with expression correlation (MEC) and methods that use gene interaction network-based analysis (INA).\n\nResultsThe performance of driver prediction methods varies considerably, with concordance with a gold-standard varying from 9% to 68%. FI methods show relatively poor performance (concordance <22%) while CBA methods provide conservative results, but require large sample sizes for high sensitivity. INA methods, through the integration of genomic and transcriptomic data, and FIC methods, by training cancer-specific models, provide the best trade-off between sensitivity and specificity. As the methods were found to predict different subsets of drivers, we propose a novel consensus-based approach, ConsensusDriver, which significantly improves the quality of predictions (20% increase in sensitivity). This tool can be applied to predict driver alterations in patient subgroups (e.g. defined by histology or clinical phenotype) or even individual patients.\n\nConclusionExisting cancer driver prediction methods are based on very different assumptions and each of them can only detect a particular subset of driver events. Consensus-based methods, like ConsensusDriver, are thus a promising approach to harness the strengths of different driver prediction paradigms.

bioinformatics