Faculty, Staff and Student Publications
Publication Date
5-23-2025
Journal
npj Systems Biology and Applications
DOI
10.1038/s41540-025-00531-z
PMID
40410237
PMCID
PMC12102339
PubMedCentral® Posted Date
5-23-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Despite advances triple negative breast cancer treatment, ~50% of patients will not achieve a pathological complete response prior to surgery with standard of care neoadjuvant therapy (NAT). We hypothesize that personalized regimens for NAT could significantly improve patient outcomes, which we address with a patient-specific digital twin framework. This framework is established by calibrating a biology-based model to longitudinal magnetic resonance images with approximate Bayesian computation. We then apply optimal control theory to either (1) reduce the final tumor cell number with equivalent dose, or (2) reduce the total dose of NAT with equivalent response. For (1), the personalized regimens (n = 50) achieved a median (range) reduction in the final tumor cell number of 17.62% (0.00-37.36%). For (2), the personalized regimens achieved a median reduction in dose delivered of 12.62% (0.00-56.55%) when compared to the standard-of-care regimen, while providing statistically equivalent tumor control.
Keywords
Triple Negative Breast Neoplasms, Humans, Neoadjuvant Therapy, Female, Bayes Theorem, Precision Medicine, Magnetic Resonance Imaging, Oncology, Mathematics and computing, Applied mathematics, Computational science, Computational biology and bioinformatics, Software
Published Open-Access
yes
Recommended Citation
Christenson, Chase; Wu, Chengyue; Hormuth, David A; et al., "Personalizing Neoadjuvant Chemotherapy Regimens for Triple-Negative Breast Cancer Using a Biology-Based Digital Twin" (2025). Faculty, Staff and Student Publications. 6225.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6225
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Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Oncology Commons