Mechanistic characterization of oscillatory patterns in unperturbed tumor growth dynamics: The interplay between cancer cells and components of tumor microenvironment

Aymara Sancho-Araiz, Zinnia P Parra-Guillen, Jean Bragard, Sergio Ardanza, Victor Mangas-Sanjuan and Iñaki F Trocóniz

PLOS Computational Biology, 2023, vol. 19, issue 10, 1-18

Abstract: Mathematical modeling of unperturbed and perturbed tumor growth dynamics (TGD) in preclinical experiments provides an opportunity to establish translational frameworks. The most commonly used unperturbed tumor growth models (i.e. linear, exponential, Gompertz and Simeoni) describe a monotonic increase and although they capture the mean trend of the data reasonably well, systematic model misspecifications can be identified. This represents an opportunity to investigate possible underlying mechanisms controlling tumor growth dynamics through a mathematical framework. The overall goal of this work is to develop a data-driven semi-mechanistic model describing non-monotonic tumor growth in untreated mice. For this purpose, longitudinal tumor volume profiles from different tumor types and cell lines were pooled together and analyzed using the population approach. After characterizing the oscillatory patterns (oscillator half-periods between 8–11 days) and confirming that they were systematically observed across the different preclinical experiments available (p 0.05)), allows the evaluation of the different oncologic treatments in a mechanistic way. Drug effects can potentially, be included in relevant processes taking place during tumor growth. In brief, the new model, in addition to describing non-monotonic tumor growth and the interaction between biological factors of the tumor microenvironment, can be used to explore different drug scenarios in monotherapy or combination during preclinical drug development.Author summary: Mathematical models for tumor growth kinetics have been widely used for several decades, including among others the exponential, the Gompertz and the Simeoni model. However, as the knowledge of the multiple processes taking place during tumor microenvironment increases, models including plausible mechanisms are becoming increasingly important. In this work, we highlight the oscillatory dynamics observed in the tumor growth over time curves and we propose a novel semi-mechanistic model capable of describing the non-monotonic growth including the interaction between cancer cells, angiogenesis, and resources such as nutrients or oxygen, in the tumor microenvironment. Our model, with respect to previous literature models, improves diagnostic plots such as weighted residuals versus time plots and residuals lag plots, and individual predictions. Additionally, the framework allows the incorporation of anticancer treatments considering their mechanisms of action. Therefore, the model constitutes a valuable tool in the development of therapeutic strategies, supporting the rational design and selection of different treatment scenarios in preclinical drug development.

Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011507 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 11507&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1011507

DOI: 10.1371/journal.pcbi.1011507

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().