Title |
Perturbation biology nominates upstream–downstream drug combinations in RAF inhibitor resistant melanoma cells
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Published in |
eLife, August 2015
|
DOI | 10.7554/elife.04640 |
Pubmed ID | |
Authors |
Anil Korkut, Weiqing Wang, Emek Demir, Bülent Arman Aksoy, Xiaohong Jing, Evan J Molinelli, Özgün Babur, Debra L Bemis, Selcuk Onur Sumer, David B Solit, Christine A Pratilas, Chris Sander |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 25% |
Unknown | 3 | 75% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 3 | 75% |
Members of the public | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 3 | 2% |
United States | 2 | 2% |
France | 1 | <1% |
Switzerland | 1 | <1% |
Canada | 1 | <1% |
Netherlands | 1 | <1% |
Unknown | 122 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 34 | 26% |
Student > Ph. D. Student | 26 | 20% |
Student > Master | 16 | 12% |
Student > Postgraduate | 9 | 7% |
Student > Bachelor | 8 | 6% |
Other | 20 | 15% |
Unknown | 18 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 36 | 27% |
Biochemistry, Genetics and Molecular Biology | 35 | 27% |
Medicine and Dentistry | 10 | 8% |
Computer Science | 9 | 7% |
Engineering | 4 | 3% |
Other | 14 | 11% |
Unknown | 23 | 18% |