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• W2116944463 abstract "Resistance to targeted cancer therapies is an important clinical problem. The discovery of anti-resistance drug combinations is challenging as resistance can arise by diverse escape mechanisms. To address this challenge, we improved and applied the experimental-computational perturbation biology method. Using statistical inference, we build network models from high-throughput measurements of molecular and phenotypic responses to combinatorial targeted perturbations. The models are computationally executed to predict the effects of thousands of untested perturbations. In RAF-inhibitor resistant melanoma cells, we measured 143 proteomic/phenotypic entities under 89 perturbation conditions and predicted c-Myc as an effective therapeutic co-target with BRAF or MEK. Experiments using the BET bromodomain inhibitor JQ1 affecting the level of c-Myc protein and protein kinase inhibitors targeting the ERK pathway confirmed the prediction. In conclusion, we propose an anti-cancer strategy of co-targeting a specific upstream alteration and a general downstream point of vulnerability to prevent or overcome resistance to targeted drugs." @default.
• W2116944463 created "2016-06-24" @default.
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• W2116944463 date "2015-08-18" @default.
• W2116944463 modified "2023-10-02" @default.
• W2116944463 title "Perturbation biology nominates upstream–downstream drug combinations in RAF inhibitor resistant melanoma cells" @default.
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• W2116944463 doi "https://doi.org/10.7554/elife.04640" @default.
• W2116944463 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4539601" @default.
• W2116944463 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26284497" @default.
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