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Elife. 2015 Aug 18;4. doi: 10.7554/eLife.04640.

Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells.

Author information

1
Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
2
Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, United States.
3
The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, United States.

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.

KEYWORDS:

cancer drug resistance; cell biology; cellular signaling; computational biology; drug synergy; human; melanoma; network modeling; proteomics; systems biology

Comment in

PMID:
26284497
PMCID:
PMC4539601
DOI:
10.7554/eLife.04640
[Indexed for MEDLINE]
Free PMC Article

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