Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells
| Autor: | Bulent Arman Aksoy, Weiqing Wang, Debra L Bemis, Christine A. Pratilas, Özgün Babur, Evan J Molinelli, Xiaohong Jing, Selcuk Onur Sumer, David B. Solit, Emek Demir, Chris Sander, Anil Korkut |
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| Rok vydání: | 2014 |
| Předmět: |
drug synergy
cancer drug resistance Drug Resistance Drug resistance Bioinformatics 0302 clinical medicine Gene Regulatory Networks Biology (General) Vemurafenib Melanoma media_common network modeling 0303 health sciences General Neuroscience General Medicine 3. Good health Drug Combinations 030220 oncology & carcinogenesis Medicine raf Kinases medicine.drug Research Article Computational and Systems Biology Drug Cell signaling QH301-705.5 Science Systems biology media_common.quotation_subject Cytological Techniques Antineoplastic Agents Biology Models Biological General Biochemistry Genetics and Molecular Biology 03 medical and health sciences proteomics Cell Line Tumor medicine Humans human 030304 developmental biology General Immunology and Microbiology Computational Biology Cell Biology Models Theoretical medicine.disease Cancer cell Cancer research cellular signaling Skin cancer |
| Zdroj: | eLife eLife, Vol 4 (2015) |
| ISSN: | 2050-084X |
| Popis: | 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. DOI: http://dx.doi.org/10.7554/eLife.04640.001 eLife digest Drugs that target the activity of specific genes could potentially form precise cancer therapies. Some cancers, including the aggressive skin cancer called melanoma, initially respond well to such treatments. However, resistance to drugs develops quickly, leading to the rapid regrowth of the tumors. Resistance can develop in a number of ways. For example, to prevent the drug from working or to compensate for the effects of a drug, cancer cells can adapt their signaling processes or acquire genetic mutations or other modifications that affect how genes are expressed. A well-designed combination of drugs that targets multiple molecular pathways can make it harder for cells to resist treatment, as this limits the number of available ‘escape’ pathways that bypass the drug targets. However, it is difficult to accurately predict how a cell will respond when treated with a particular drug, making it extremely challenging to design effective drug combinations. In 2013, researchers developed a technique to build predictive models of cellular response pathways based on data collected from perturbation experiments followed by mathematical modeling. Now, Korkut et al.—including several of the researchers involved in the 2013 work—have refined this technology and applied it to the problem of preventing drug resistance in cancer cells. Computer simulations that used the mathematical models suggested a particular strategy of ‘upstream–downstream targeting’ in cells that were insensitive to the clinically successful drug vemurafenib (an inhibitor of RAF proteins, which are often mutated in cancers). In the landscape of signaling pathways, the target of the upstream drug is on or near the mutated RAF protein. c-Myc, the indirect target of the downstream drug helps to express genes that trigger signals that cause the cells to grow. Inhibiting both targets in parallel may have the dual advantage of blocking the activation of the tumor-specific growth pathway while reducing the cancer cells' attempts to bypass the activation block. An initial test of the designed drug combination required moving from computer simulations to the laboratory using cell cultures originally derived from melanoma tumors. When Korkut et al. applied the drug combination, the combined treatment successfully blocked cell growth. The results suggest that the data-driven computer modeling strategy termed perturbation biology could be a useful tool for identifying effective cancer drug combinations for further preclinical research, possibly followed by clinical trials. DOI: http://dx.doi.org/10.7554/eLife.04640.002 |
| Databáze: | OpenAIRE |
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