ArticleScience advances2025
Multiscale mathematical model-informed reinforcement learning optimizes combination treatment scheduling in glioblastoma evolution.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions.Journal of the Egyptian National Cancer Institute · 2026Review
- Multiscale modeling of T cell exhaustion: A mathematical framework integrating continuous dynamics with spatial heterogeneity.PLoS computational biology · 2026Article
- Evolving Landscape of Glioblastoma Research: Integrating Therapeutic Advances and Diagnostic Frontiers.Brain sciences · 2026Review
- Article
- Review
- The spatial revolution in immuno-oncology: artificial intelligence decoding NK cell niches to predict therapeutic response.Frontiers in immunology · 2026Review
- Causal AI for cancer immunotherapy: a narrative framework review of target trial emulation, treatment-effect learning and clinical translation.Frontiers in immunology · 2026Review
- A Roadmap for the Future of Systems Biology in Cancer Research.Cancer research · 2025Review
- Hi-C3: a statistical inference-based model for reconstructing higher-order cell-cell communication networks.Briefings in bioinformatics · 2025Article
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Authors and funding
5 authors.
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Abstract
Dynamic tumor-microenvironment interactions greatly affect growth and drug resistance, highlighting the importance and challenge of developing mathematical models to optimize treatment schedules. Here, we describe a multiscale mathematical model-informed reinforcement learning (M4RL) framework to simulate dynamic tumor-microenvironment interactions and optimize drug combination scheduling. We first develop a multiscale agent-based model (MSABM) for a critical biological scenario where interactions between tumor-associated macrophages (TAMs) and tumor cells (TCs) underlie immunotherapy resistance in glioblastoma. Next, we learn a surrogate model based on Fokker-Planck equations for the MSABM using a physics-informed neural network approach. We then design a surrogate model-based reinforcement learning method, using an efficient parallel actor-critic algorithm, to predict optimal scheduling of combination treatments. The most effective regimen of dynamic combination of CSF1R inhibitor (targeting TAMs) and IGF1R inhibitor (targeting TCs) is identified and then verified using spatial transcriptomic data. Overall, the M4RL framework introduces a computational approach for characterizing tumor-microenvironment interactions and optimizing dynamic scheduling of drug combinations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.