Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Mathematical Modeling of Non-small Cell Lung Cancer Biology Through the Experimental Data on Cell Composition and Growth of Patient-Derived Organoids

Rushan A. Sulimanov
,
Konstantin B. Koshelev
,
Vladimir A. Makarov
,
Alexander V. Mezentsev
,
Mikhail O. Durymanov
ORCID logo ,
Komal Zahid
,
Lilian Ismail
ORCID logo ,
Yegor Y. Rumyantsev
* ,
Ilya A. Laskov
Version 1 : Received: 10 October 2023 / Approved: 11 October 2023 / Online: 12 October 2023 (05:05:42 CEST)

A peer-reviewed article of this Preprint also exists.

Sulimanov, R.; Koshelev, K.; Makarov, V.; Mezentsev, A.; Durymanov, M.; Ismail, L.; Zahid, K.; Rumyantsev, Y.; Laskov, I. Mathematical Modeling of Non-Small-Cell Lung Cancer Biology through the Experimental Data on Cell Composition and Growth of Patient-Derived Organoids. Life 2023, 13, 2228. Sulimanov, R.; Koshelev, K.; Makarov, V.; Mezentsev, A.; Durymanov, M.; Ismail, L.; Zahid, K.; Rumyantsev, Y.; Laskov, I. Mathematical Modeling of Non-Small-Cell Lung Cancer Biology through the Experimental Data on Cell Composition and Growth of Patient-Derived Organoids. Life 2023, 13, 2228.

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 NSCLC. 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.

Keywords

mathematical modeling; non-small cell lung cancer; tumor microenvironment; patient-derived tumor organoids

Subject

Medicine and Pharmacology, Oncology and Oncogenics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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