Cancer therapy with immune checkpoint inhibitor and CSF-1 blockade: A mathematical model. (7th January 2023)

• Record Type: Journal Article
• Authors: Siewe, Nourridine; Friedman, Avner
• Is Part Of: Journal of theoretical biology. Volume 556(2023)
• Publication Date: 2023-01-07
• Subjects: Cancer model framework -- Immune checkpoint inhibitor -- CSF-1 blockade -- Nivolumab -- Pexidartinib -- Cabiralizumab; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2022.111297
• Languages: English
• ISSNs: 0022-5193

Abstract

Abstract: Immune checkpoint inhibitors (ICIs) introduced in recent years have revolutionized the treatment of many metastatic cancers. However, data suggest that treatment has benefits only in a limited percentage of patients, and that this is due to immune suppression of the tumor microenvironment (TME). Anti-tumor inflammatory macrophages (M1), which are attracted to the TME, are converted by tumor secreted cytokines, such as CSF-1, to pro-tumor anti-inflammatory macrophages (M2), or tumor associated macrophages (TAMs), which block the anti-tumor T cells. In the present paper we develop a mathematical model that represents the interactions among the immune cells and cancer in terms of differential equations. The model can be used to assess treatments of combination therapy of anti-PD-1 with anti-CSF-1. Examples are given in comparing the efficacy among different strategies for anti-CSF-1 dosing in a setup of clinical trials. Highlights: A model of M1 to M2 macrophages conversion by the cytokine CSF-1 is developed. Treatment's efficacy & toxicity with combining anti-PD-1 and anti-CSF-1 is assessed. The best strategy is to give anti-CSF-1 at larger amounts as early as possible.