Mechanistic modeling of brain metastases in NSCLC provides computational markers for personalized prediction of outcome

Benzekry, Sébastien; Schlicke, Pirmin; Tomasini, Pascale; Simon, E.

doi:10.1101/2023.01.10.23284189

Cited by 1 publication

(1 citation statement)

References 80 publications

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“…However, it is extremely difficult to accurately identify spatial tumor growth on a patient-specific level, especially under a treatment plan [10,11]. A considerable variety of mathematical models has helped to improve the understanding of biological principles in cancer growth and treatment response [12][13][14][15][16] and their predictive power [17,18]. Globally, lung cancer is the leading cancer-related mortality cause.…”

Section: Introductionmentioning

confidence: 99%

A phase-field model for non-small cell lung cancer under the effects of immunotherapy

Wagner

Schlicke

Fritz

et al. 2023

Preprint

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Formulating tumor models that predict growth under therapy is vital for improving patient-specific treatment plans. In this context, we present our recent work on simulating non-small-scale cell lung cancer (NSCLC) in a simple, deterministic setting for two different patients receiving an immunotherapeutic treatment. At its core, our model consists of a Cahn-Hilliard-based phase-field model describing the evolution of proliferative and necrotic tumor cells. These are coupled to a simplified nutrient model that drives the growth of the proliferative cells and their decay into necrotic cells. The applied immunotherapy decreases the proliferative cell concentration. Here, we model the immunotherapeutic agent concentration in the entire lung over time by an ordinary differential equation (ODE). Finally, reaction terms provide a coupling between all these equations. By assuming spherical, symmetric tumor growth and constant nutrient inflow, we simplify this full 3D cancer simulation model to a reduced 1D model. We can then resort to patient data gathered from computed tomography (CT) scans over several years to calibrate our model. For the reduced 1D model, we show that our model can qualitatively describe observations during immunotherapy by fitting our model parameters to existing patient data. Our model covers cases in which the immunotherapy is successful and limits the tumor size, as well as cases predicting a sudden relapse, leading to exponential tumor growth. Finally, we move from the reduced model back to the full 3D cancer simulation in the lung tissue. Thereby, we show the predictive benefits a more detailed patient-specific simulation including spatial information could yield in the future.

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