TitleEffective Combination Therapies for B-cell Lymphoma Predicted by a Virtual Disease Model.
Publication TypeJournal Article
Year of Publication2017
AuthorsDu, Wei, Goldstein Rebecca, Jiang Yanwen, Aly Omar, Cerchietti Leandro, Melnick Ari, and Elemento Olivier
JournalCancer Res
Date Published2017 Apr 15
KeywordsCell Growth Processes, Combined Modality Therapy, Humans, Lymphoma, Large B-Cell, Diffuse, MAP Kinase Signaling System, Models, Biological, NF-kappa B, Protein Interaction Maps, Proto-Oncogene Proteins c-akt, Receptors, Antigen, B-Cell, Signal Transduction

<p>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 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. .</p>

Alternate JournalCancer Res
PubMed ID28130226
PubMed Central IDPMC5392381
Grant ListR01 CA104348 / CA / NCI NIH HHS / United States
R01 CA143032 / CA / NCI NIH HHS / United States
R01 CA194547 / CA / NCI NIH HHS / United States
U24 CA210989 / CA / NCI NIH HHS / United States