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  5. Use of Mathematical Model for Elucidation of Molecular Mechanisms of Tumor Necrosis Factor Cytokine Storm in Lung Cancer Patients

Use of Mathematical Model for Elucidation of Molecular Mechanisms of Tumor Necrosis Factor Cytokine Storm in Lung Cancer Patients

J Joo1*, D Ray2 , R Ten Haken3 , T Lawrence4 , I El Naqa5 , (1) University of Michigan Medical Center, Ann Arbor, MI, (2) University of Michigan Radiation Oncology Department, Ann Arbor, ,(3) University of Michigan, Ann Arbor, MI, (4) University of Michigan, Ann Arbor, ,(5) University of Michigan, Ann Arbor, MI

Presentations

(Wednesday, 7/17/2019) 10:30 AM - 11:00 AM

Room: Exhibit Hall | Forum 3

Purpose: To develop a dynamic mathematical model for capturing cytokine release in lung cancer patients undergoing radiotherapy. This approach will elucidate underlying molecular mechanisms of cytokine storm during radiotherapy, and aid devising an in silico approach for optimizing radiation scheduling and mitigating cytokine inflammatory effects by TNFα inhibition.

Methods: We developed a system of coupled nonlinear ordinary differential equations (ODEs) to represent the nonlinear biochemical and physical interactions among tumor growth, radiation-tumor interactions, and biochemical species in TNFα signaling networks. Tumor repopulation was modeled by continuous time Gompertzian growth model. Radiation-induced cell killing was modeled by the linear-quadratic model of cell survival at each instantaneous dose fraction. The cellular signaling events in TNFα signaling networks were modeled by a system of seven ODEs. Taking advantage of large time scale separation between tumor growth/killing (in days) and cellular signaling events (in hours), we applied the quasi-steady state approximation to TNFα signaling networks and performed both steady state and stability analyses. Model parameters were based on clinical, pre-clinical, and reported literature data.

Results: We parameterized the ODE system with experimental time-series data. The dynamics of the TNFα signaling networks are characterized by bistability and its sensitive dependence on the input signal magnitude and initialization conditions. Because of this bistability, the dose-response curve exhibits both ultra-sensitive switching behavior and response hysteresis. From the numerical simulations, we found that this bistability and hysteresis are the driving forces of TNFα cytokine storm, a pathological temporal pattern of cytokine release most likely leading to radiation-induced pneumonitis. This can be mitigated by optimizing the radiation schedule in presence of TNFα inhibitors.

Conclusion: We used network biology and dynamical model to identify the bistability as the key molecular dynamical mechanisms of TNFα cytokine storm in lung cancer patients undergoing radiotherapy, which can be utilized to optimize therapeutic interventions.

Keywords

Linear Quadratic Model, Nonlinear Dynamics, Radiobiology

Taxonomy

TH- Radiobiology(RBio)/Biology(Bio): RBio- LQ/TCP/NTCP/outcome modeling

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