Effective Combination Therapies for B-cell Lymphoma Predicted by a Virtual Disease Model. Academic Article uri icon

Overview

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.

publication date

  • January 27, 2017

Research

keywords

  • Lymphoma, Large B-Cell, Diffuse
  • Models, Biological

Identity

PubMed Central ID

  • PMC5392381

Scopus Document Identifier

  • 85018836494

Digital Object Identifier (DOI)

  • 10.1158/0008-5472.CAN-16-0476

PubMed ID

  • 28130226

Additional Document Info

volume

  • 77

issue

  • 8