Evidence map›Paper›PMID 41140641›Full record

ArticleDynamic games and applications2025

Stackelberg Evolutionary Games of Cancer Treatment: What Treatment Strategy to Choose if Cancer Can be Stabilized?

Monica Salvioli, Hasti Garjani, Mohammadreza Satouri, Mark Broom, Yannick Viossat, Joel S Brown, Johan Dubbeldam, Kateřina Staňková

Abstract read
In one paragraph

Article in Dynamic games and applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Monica Salvioli *Evolutionary Game Theory Lab, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands.
Hasti Garjani *Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands.
Mohammadreza SatouriEvolutionary Game Theory Lab, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands.
Mark BroomDepartment of Mathematics, City, University of London, London, UK.
Yannick ViossatCeremade, Université Paris Dauphine-PSL, Paris, France.
Joel S BrownIntegrated Mathematical Oncology, H. Lee Moffitt Cancer and Research Institute, Tampa, FL USA.
Johan DubbeldamDelft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands.
Kateřina StaňkováEvolutionary Game Theory Lab, Faculty of Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present a game-theoretic model of a polymorphic cancer cell population where the treatment-induced resistance is a quantitative evolving trait. When stabilization of the tumor burden is possible, we expand the model into a Stackelberg evolutionary game, where the physician is the leader and the cancer cells are followers. The physician chooses a treatment dose to maximize an objective function that is a proxy of the patient's quality of life. In response, the cancer cells evolve a resistance level that maximizes their proliferation and survival. Assuming that cancer is in its ecological equilibrium, we compare the outcomes of three different treatment strategies: giving the maximum tolerable dose throughout, corresponding to the standard of care for most metastatic cancers, an ecologically enlightened therapy, where the physician anticipates the short-run, ecological response of cancer cells to their treatment, but not the evolution of resistance to treatment, and an evolutionarily enlightened therapy, where the physician anticipates both ecological and evolutionary consequences of the treatment. Of the three therapeutic strategies, the evolutionarily enlightened therapy leads to the highest values of the objective function, the lowest treatment dose, and the lowest treatment-induced resistance. Conversely, in our model, the maximum tolerable dose leads to the worst values of the objective function, the highest treatment dose, and the highest treatment-induced resistance.

Indexed as

Evolutionary cancer therapyEvolutionary game theoryHeterogeneityMathematical oncologyResistanceStackelberg evolutionary games

Identifiers

PMID41140641
PMCPMC12552401

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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.