Speaker
Description
Adaptive therapy is an evolution-based treatment paradigm in metastatic cancer, which dynamically adjusts treatment to control, rather than minimize, tumor burden. Promising clinical results in prostate cancer indicate the potential of adaptive treatment protocols to delay relapse, but demonstrate broad heterogeneity in patient response. This naturally leads to the question: why does this heterogeneity occur, and is a ‘one-size-fits-all' protocol best for all patients?
Using a Lotka-Volterra model for tumor dynamics, we predict the expected benefit of adaptive therapy and extend this to a trio of mathematical biomarkers that can predict the time to progression and mean daily dose under a range of clinically realistic treatment protocols. Our mathematical framework accurately identifies patients with the greatest delay to progression, or reduction in mean daily dose, enabled by adaptive therapy. Our novel mathematical biomarker approach stratifies patients into distinct treatment protocols based on their initial treatment response, allowing for a personalized, mathematically informed approach to treatment scheduling.
Bibliography
@article{Gallagher2025a,
title = {Deriving Optimal Treatment Timing for Adaptive Therapy: Matching the Model to the Tumor Dynamics},
volume = {87},
ISSN = {1522-9602},
url = {http://dx.doi.org/10.1007/s11538-025-01525-y},
DOI = {10.1007/s11538-025-01525-y},
number = {10},
journal = {Bulletin of Mathematical Biology},
publisher = {Springer Science and Business Media LLC},
author = {Gallagher, Kit and Strobl, Maximilian A. R. and Anderson, Alexander R. A. and Maini, Philip K.},
year = {2025},
month = sep
}
@article{Gallagher2025b,
title = {Predicting Treatment Outcomes from Adaptive Therapy — A New Mathematical Biomarker},
url = {http://dx.doi.org/10.1101/2025.04.03.646615},
DOI = {10.1101/2025.04.03.646615},
publisher = {openRxiv},
author = {Gallagher, Kit and Strobl, Maximilian A. R. and Maini, Philip K. and Anderson, Alexander R. A.},
year = {2025},
month = apr
}
