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PLoS Comput Biol. 2018 Jan 19;14(1):e1005920. doi: 10.1371/journal.pcbi.1005920. eCollection 2018 Jan.

A mathematical model for IL-6-mediated, stem cell driven tumor growth and targeted treatment.

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Simon A. Levin Mathematical, Computational, and Modeling Sciences Center, School of Human Evolution and Social Change, Arizona State University, Tempe, Arizona, United States of America.
Division of Hematology/Oncology, Department of Internal Medicine, University of Michigan Cancer Center, Ann Arbor, Michigan, United States of America.
Departments of Cardiology, Restorative Sciences, and Endontics, University of Michigan, Ann Arbor, Michigan, United States of America.
Department of Mathematics, University of Michigan, Ann Arbor, Michigan, United States of America.


Targeting key regulators of the cancer stem cell phenotype to overcome their critical influence on tumor growth is a promising new strategy for cancer treatment. Here we present a modeling framework that operates at both the cellular and molecular levels, for investigating IL-6 mediated, cancer stem cell driven tumor growth and targeted treatment with anti-IL6 antibodies. Our immediate goal is to quantify the influence of IL-6 on cancer stem cell self-renewal and survival, and to characterize the subsequent impact on tumor growth dynamics. By including the molecular details of IL-6 binding, we are able to quantify the temporal changes in fractional occupancies of bound receptors and their influence on tumor volume. There is a strong correlation between the model output and experimental data for primary tumor xenografts. We also used the model to predict tumor response to administration of the humanized IL-6R monoclonal antibody, tocilizumab (TCZ), and we found that as little as 1mg/kg of TCZ administered weekly for 7 weeks is sufficient to result in tumor reduction and a sustained deceleration of tumor growth.

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