ArticleProceedings. Biological sciences2020
Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory.
Article in Proceedings. Biological sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Article
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- Adaptive and sequential cancer therapies emerge from treatment schedule optimization.Research square · 2026Article
- Modeling optimal timing of immunotherapy and chemotherapy to prevent resistance and recurrence in triple-negative breast cancer.Scientific reports · 2026Article
- Stochastic Evolutionary Control in Heterogeneous Populations.bioRxiv : the preprint server for biology · 2026Article
- Optimal control theory as a method for designing multidrug adaptive therapy regimens.NPJ systems biology and applications · 2026Article
- Can evolutionary therapy be applied in non-small cell lung cancer?Scientific reports · 2026Article
- Migrastatic therapy as a potential game-changer in adaptive cancer treatment.Scientific reports · 2026Article
- Cancer model with moving extinction threshold reproduces real cancer data.Journal of the Royal Society, Interface · 2025Article
- The balance between intrinsic and ecological fitness reveals hidden regimes in eco-evolutionary population dynamics.Research square · 2025Article
- Personalizing chemotherapy drug selection using a novel transcriptomic chemogram.PLoS computational biology · 2025Article
- The balance between intrinsic and ecological fitness reveals hidden regimes in eco-evolutionary population dynamics.bioRxiv : the preprint server for biology · 2025Article
- Validation of polymorphic Gompertzian model of cancer through in vitro and in vivo data.PloS one · 2025Article
- Stackelberg Evolutionary Games of Cancer Treatment: What Treatment Strategy to Choose if Cancer Can be Stabilized?Dynamic games and applications · 2025Article
- Stability of the Darwinian Dynamics: Effect of Intraspecific Competition and Human Intervention.Dynamic games and applications · 2025Article
- Article
- Cell-cell fusion in cancer: The next cancer hallmark?The international journal of biochemistry & cell biology · 2024Review
- Threshold-awareness in adaptive cancer therapy.PLoS computational biology · 2024Article
- Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- N-of-1 medicine.Singapore medical journal · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Recent clinical trials have shown that adaptive drug therapies can be more efficient than a standard cancer treatment based on a continuous use of maximum tolerated doses (MTD). The adaptive therapy paradigm is not based on a preset schedule; instead, the doses are administered based on the current state of tumour. But the adaptive treatment policies examined so far have been largely ad hoc. We propose a method for systematically optimizing adaptive policies based on an evolutionary game theory model of cancer dynamics. Given a set of treatment objectives, we use the framework of dynamic programming to find the optimal treatment strategies. In particular, we optimize the total drug usage and time to recovery by solving a Hamilton-Jacobi-Bellman equation. We compare MTD-based treatment strategy with optimal adaptive treatment policies and show that the latter can significantly decrease the total amount of drugs prescribed while also increasing the fraction of initial tumour states from which the recovery is possible. We conclude that the use of optimal control theory to improve adaptive policies is a promising concept in cancer treatment and should be integrated into clinical trial design.
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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.