Mathematical Modeling of Non-Small-Cell Lung Cancer Biology through the Experimental Data on Cell Composition and Growth of Patient-Derived Organoids
Author:
Sulimanov Rushan1, Koshelev Konstantin12ORCID, Makarov Vladimir1, Mezentsev Alexandre13, Durymanov Mikhail13ORCID, Ismail Lilian3ORCID, Zahid Komal3, Rumyantsev Yegor1, Laskov Ilya1
Affiliation:
1. Medical Informatics Laboratory, Yaroslav-the-Wise Novgorod State University, 173003 Veliky Novgorod, Russia 2. Ivannikov Institute for System Programming of the Russian Academy of Science, 109004 Moscow, Russia 3. School of Biological and Medical Physics, Moscow Institute of Physics and Technology, 141701 Dolgoprudny, Russia
Abstract
Mathematical models of non-small-cell lung cancer are powerful tools that use clinical and experimental data to describe various aspects of tumorigenesis. The developed algorithms capture phenotypic changes in the tumor and predict changes in tumor behavior, drug resistance, and clinical outcomes of anti-cancer therapy. The aim of this study was to propose a mathematical model that predicts the changes in the cellular composition of patient-derived tumor organoids over time with a perspective of translation of these results to the parental tumor, and therefore to possible clinical course and outcomes for the patient. Using the data on specific biomarkers of cancer cells (PD-L1), tumor-associated macrophages (CD206), natural killer cells (CD8), and fibroblasts (αSMA) as input, we proposed a model that accurately predicts the cellular composition of patient-derived tumor organoids at a desired time point. Combining the obtained results with “omics” approaches will improve our understanding of the nature of non-small-cell lung cancer. Moreover, their implementation into clinical practice will facilitate a decision-making process on treatment strategy and develop a new personalized approach in anti-cancer therapy.
Funder
Ministry of Science and Higher Education of the Russian Federation
Subject
Paleontology,Space and Planetary Science,General Biochemistry, Genetics and Molecular Biology,Ecology, Evolution, Behavior and Systematics
Reference32 articles.
1. Rossi, R., De Angelis, M.L., Xhelili, E., Sette, G., Eramo, A., De Maria, R., Cesta Incani, U., Francescangeli, F., and Zeuner, A. (2022). Lung Cancer Organoids: The Rough Path to Personalized Medicine. Cancers, 14. 2. The application of patient-derived organoid in the research of lung cancer;Li;Cell. Oncol.,2023 3. Mathematical modeling and dynamical analysis of anti-tumor drug dose-response;Xiao;Math. Biosci. Eng.,2022 4. Rodriguez Messan, M., Yogurtcu, O.N., McGill, J.R., Nukala, U., Sauna, Z.E., and Yang, H. (2021). Mathematical model of a personalized neoantigen cancer vaccine and the human immune system. PLoS Comput. Biol., 17. 5. Voit, E.O. (2017). The best models of metabolism. Wiley Interdiscip. Rev. Syst. Biol. Med., 9.
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