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Cancer Res. 2017 Apr 15;77(8):1818-1830. doi: 10.1158/0008-5472.CAN-16-0476. Epub 2017 Jan 27.

Effective Combination Therapies for B-cell Lymphoma Predicted by a Virtual Disease Model.

Author information

1
Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York.
2
Hematology/Oncology Division, Department of Medicine, Weill Cornell Medicine, New York, New York.
3
Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York.
4
Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York. ole2001@med.cornell.edu.
5
Institute for Precision Medicine, Weill Cornell Medicine, New York, New York.

Abstract

The complexity of cancer signaling networks limits the efficacy of most single-agent treatments and brings about challenges in identifying effective combinatorial therapies. In this study, we used chronic active B-cell receptor (BCR) signaling in diffuse large B-cell lymphoma as a model system to establish a computational framework to optimize combinatorial therapy in silico We constructed a detailed kinetic model of the BCR signaling network, which captured the known complex cross-talk between the NFκB, ERK, and AKT pathways and multiple feedback loops. Combining this signaling model with a data-derived tumor growth model, we predicted viability responses of many single drug and drug combinations in agreement with experimental data. Under this framework, we exhaustively predicted and ranked the efficacy and synergism of all possible combinatorial inhibitions of eleven currently targetable kinases in the BCR signaling network. Ultimately, our work establishes a detailed kinetic model of the core BCR signaling network and provides the means to explore the large space of possible drug combinations. Cancer Res; 77(8); 1818-30. ©2017 AACR.

PMID:
28130226
PMCID:
PMC5392381
DOI:
10.1158/0008-5472.CAN-16-0476
[Indexed for MEDLINE]
Free PMC Article

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