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July 5th, 2025
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Dana-Farber Cancer Institute
bioinformatics
biorxiv

Mechanistic modeling and machine learning identifies optimum radiotherapy schedules to prevent treatment-induced metastasis

Graser, C.Open in Google Scholar•Zhou, Z.Open in Google Scholar•Schürch, M.Open in Google Scholar•Moorhead, G.Open in Google Scholar•Dean, J.Open in Google Scholar•Lin, C.Open in Google Scholar•Dean, J.Open in Google Scholar•Kozono, D.Open in Google Scholar•Lahav, G.Open in Google Scholar•Michor, F.Open in Google Scholar

Lung cancer patients often experience increased metastasis formation after radiotherapy. However, it is incompletely understood whether radiation affects the migratory behavior of tumor cells and how altered radiotherapy schedules might mitigate this risk. To address these questions, we performed live-cell microscopy experiments to profile changes in cell migration during radiation across 12 cancer cell lines and developed a predictive computational modeling platform describing tumor volume and dissemination during radiotherapy. Using this platform, we identified optimal fractionation schedules and then performed extensive in silico clinical trials, establishing that our optimized schedules substantially reduce metastatic seeding relative to the standard of care schedule. Training transformer models on the in silico clinical trial data enabled us to recover mechanistic parameters with high accuracy, demonstrating that the features determining optimal radiotherapy can be inferred from longitudinal tumor data. Our integrative predictive approach enables the rational design of optimum clinical trials across indications.

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