Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?

Several mathematical models to predict tumor growth over time have been developed in the last decades. A central aspect of such models is the interaction of tumor cells with immune effector cells. The Kuznetsov model (Kuznetsov et al. in Bull Math Biol 56(2):295–321, 1994) is the most prominent of t...

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Bibliographic Details
Published in:Bulletin of mathematical biology 2022, Vol.84 (11), p.130-130, Article 130
Main Authors: El Wajeh, Mohammad, Jung, Falco, Bongartz, Dominik, Kappatou, Chrysoula Dimitra, Ghaffari Laleh, Narmin, Mitsos, Alexander, Kather, Jakob Nikolas
Format: Article
Language:English
Subjects:
Cancer
Cancer immunotherapy
Cell Biology
Cell Count
Data points
Effector cells
Humans
Immunotherapy
Life Sciences
Mathematical and Computational Biology
Mathematical Concepts
Mathematical models
Mathematics
Mathematics and Statistics
Medical treatment
Models, Biological
Neoplasms - therapy
Original
Original Article
Parameter estimation
Parameter identification
Patients
Tumor cells
Tumors
Citations: Items that this one cites
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Summary:Several mathematical models to predict tumor growth over time have been developed in the last decades. A central aspect of such models is the interaction of tumor cells with immune effector cells. The Kuznetsov model (Kuznetsov et al. in Bull Math Biol 56(2):295–321, 1994) is the most prominent of these models and has been used as a basis for many other related models and theoretical studies. However, none of these models have been validated with large-scale real-world data of human patients treated with cancer immunotherapy. In addition, parameter estimation of these models remains a major bottleneck on the way to model-based and data-driven medical treatment. In this study, we quantitatively fit Kuznetsov’s model to a large dataset of 1472 patients, of which 210 patients have more than six data points, by estimating the model parameters of each patient individually. We also conduct a global practical identifiability analysis for the estimated parameters. We thus demonstrate that several combinations of parameter values could lead to accurate data fitting. This opens the potential for global parameter estimation of the model, in which the values of all or some parameters are fixed for all patients. Furthermore, by omitting the last two or three data points, we show that the model can be extrapolated and predict future tumor dynamics. This paves the way for a more clinically relevant application of mathematical tumor modeling, in which the treatment strategy could be adjusted in advance according to the model’s future predictions.
ISSN:0092-8240
1522-9602
DOI:10.1007/s11538-022-01075-7