The phenomenon of inflammation is increasingly being implicated as a promoter of cancer development. Accordingly, linkages between different cancer types and inflammation are traced. Findings in the biomedical literature related to inflammatory footprints in cancer development are highlighted. Existing mathematical models, even though few, that involve inflammation and cancer are reviewed. With emphasis on the need for increased modeling activity in the area, the biomedical discussions and modeling reviews then lead into the proposal of a simple mathematical model that serves as an additional candidate model for studying cancer dynamics in an inflammatory environment. This is looked at as a way of building upon the relatively few mathematical modeling approaches that may be useful for providing insights as the biomedical studies and investigations proceed. The proposed model consists of a system of three ordinary differential equations tracking the interaction dynamics of cancer cells, inflammatory cells, and normal tissue. Subsequently, model steady states are analyzed and most importantly given necessary interpretations in biomedical contexts. To address parameter variability, uncertainty analysis using Latin Hypercube sampling is used to explore output uncertainty, while partial rank correlation coefficient (PRCC) sensitivity analysis identifies key driving parameters. PRCC analysis identifies three immunologically distinct pathways: one drives a protective anti-tumor response, promoting inflammatory cells that suppress cancer while sparing normal tissue; another drives tumor expansion and tissue damage while dampening inflammation, suggesting a pathogenic role; and a third supports normal tissue maintenance but indirectly fuels cancer growth and may limit inflammatory cells via resource competition. These findings underscore the challenge of supporting normal tissue without inadvertently fueling cancer growth.
Citation: Rachid Ouifki, Evans Afenya, Suneel Mundle. Modeling cancer dynamics in an inflammatory environment[J]. Mathematical Biosciences and Engineering, 2026, 23(8): 2429-2461. doi: 10.3934/mbe.2026088
The phenomenon of inflammation is increasingly being implicated as a promoter of cancer development. Accordingly, linkages between different cancer types and inflammation are traced. Findings in the biomedical literature related to inflammatory footprints in cancer development are highlighted. Existing mathematical models, even though few, that involve inflammation and cancer are reviewed. With emphasis on the need for increased modeling activity in the area, the biomedical discussions and modeling reviews then lead into the proposal of a simple mathematical model that serves as an additional candidate model for studying cancer dynamics in an inflammatory environment. This is looked at as a way of building upon the relatively few mathematical modeling approaches that may be useful for providing insights as the biomedical studies and investigations proceed. The proposed model consists of a system of three ordinary differential equations tracking the interaction dynamics of cancer cells, inflammatory cells, and normal tissue. Subsequently, model steady states are analyzed and most importantly given necessary interpretations in biomedical contexts. To address parameter variability, uncertainty analysis using Latin Hypercube sampling is used to explore output uncertainty, while partial rank correlation coefficient (PRCC) sensitivity analysis identifies key driving parameters. PRCC analysis identifies three immunologically distinct pathways: one drives a protective anti-tumor response, promoting inflammatory cells that suppress cancer while sparing normal tissue; another drives tumor expansion and tissue damage while dampening inflammation, suggesting a pathogenic role; and a third supports normal tissue maintenance but indirectly fuels cancer growth and may limit inflammatory cells via resource competition. These findings underscore the challenge of supporting normal tissue without inadvertently fueling cancer growth.
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Rachid Ouifki, Evans Afenya, Suneel Mundle. Modeling cancer dynamics in an inflammatory environment[J]. Mathematical Biosciences and Engineering, 2026, 23(8): 2429-2461. doi: 10.3934/mbe.2026088
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