Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells.
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.
Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Anil
A
Korkut
Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Weiqing
W
Wang
Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Emek
E
Demir
Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Bülent Arman
BA
Aksoy
Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Xiaohong
X
Jing
Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Evan J
EJ
Molinelli
Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Özgün
Ö
Babur
Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Debra L
DL
Bemis
Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Selcuk
S
Onur Sumer
Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, United States.
David B
DB
Solit
The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, United States.
Christine A
CA
Pratilas
Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.
Chris
C
Sander
D000970:Antineoplastic Agents / Q000494:pharmacology*; D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods*; D003584:Cytological Techniques / Q000379:methods*; D004338:Drug Combinations; D004351:Drug Resistance; D053263:Gene Regulatory Networks; D006801:Humans; D008545:Melanoma / Q000188:drug therapy*; D008954:Models, Biological; D008962:Models, Theoretical; D048490:raf Kinases / Q000037:antagonists & inhibitors
D000970:Antineoplastic Agents; D004338:Drug Combinations; D048490:raf Kinases
cancer drug resistance; cell biology; cellular signaling; computational biology; drug synergy; human; melanoma; network modeling; proteomics; systems biology