Faculty, Staff and Student Publications

Publication Date

8-15-2025

Journal

Cancer Research

DOI

10.1158/0008-5472.CAN-24-2232

PMID

40439571

PMCID

PMC12355177

PubMedCentral® Posted Date

5-29-2025

PubMedCentral® Full Text Version

Post-print

Abstract

The gut microbiome has emerged as a key regulator of response to cancer immunotherapy. However, a better understanding of the underlying mechanisms by which the microbiome influences immunotherapy is needed to identify strategies to optimize outcomes. To this end, we developed a mathematical model to obtain insights into the effect of the microbiome on the immune system and immunotherapy response. This model was based on (i) gut microbiome data derived from preclinical studies, (ii) mathematical modeling of the antitumor immune response, (iii) association analysis of microbiome profiles with model-predicted immune profiles, and (iv) statistical models that correlate model parameters with the microbiome. The model was used to investigate the complexity of murine and human studies on microbiome modulation. Comparison of model predictions with experimental observations of tumor response in the training and test datasets supported the hypothesis that two model parameters, the activation and killing rate constants of immune cells, are the most influential in tumor progression and are potentially affected by microbiome composition. Evaluation of the associations between the gut microbiome and immune profile indicated that the components and structure of the gut microbiome affect the activation and killing rate of adaptive and innate immune cells. Overall, this study contributes to a deeper understanding of microbiome-cancer interactions and offers a framework for understanding how microbiome interactions influence cancer treatment outcomes.

Significance: Integration of mathematical modeling and microbiome data reveals how gut microbiome components impact immune response, providing insights to optimize immunotherapy strategies. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.

Keywords

Gastrointestinal Microbiome, Humans, Neoplasms, Immunotherapy, Animals, Mice, Models, Theoretical

Published Open-Access

yes

Download

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.