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

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 a

SUBMITTER: Du W 

PROVIDER: S-EPMC5392381 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature