Optimizing Cancer Treatment Using Game Theory

Staňková, Kateřina; Brown, Joel S.; Dalton, William S.; Gatenby, Robert A.

doi:10.1001/jamaoncol.2018.3395

Cited by 164 publications

(147 citation statements)

References 47 publications

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“…It may well be wiser to target the control case, to improve the patient survival times 5 . Such results have similarly been suggested by evolutionary game theory 44,45 .…”

Section: Discussion and Future Directionssupporting

confidence: 83%

Learning-accelerated Discovery of Immune-Tumour Interactions

Ozik

Collier

Heiland

et al. 2019

Preprint

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We present an integrated framework for enabling dynamic exploration of design spaces for cancer immunotherapies with detailed dynamical simulation models on high-performance computing resources. Our framework combines PhysiCell, an open source agent-based simulation platform for cancer and other multicellular systems, and EMEWS, an open source platform for extreme-scale model exploration. We build an agent-based model of immunosurveillance against heterogeneous tumours, which includes spatial dynamics of stochastic tumour-immune contact interactions. We implement active learning and genetic algorithms using high-performance computing workflows to adaptively sample the model parameter space and iteratively discover optimal cancer regression regions within biological and clinical constraints.

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“…It may well be wiser to target the control case, to improve the patient survival times 5 . Such results have similarly been suggested by evolutionary game theory 44,45 .…”

Section: Discussion and Future Directionssupporting

confidence: 83%

Learning-accelerated Discovery of Immune-Tumour Interactions

Ozik

Collier

Heiland

et al. 2019

Preprint

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“…While this new knowledge is driving cancer research forward, it has largely not yet affected clinical practice, with the majority of clinical protocols relying on MTD-based approaches, which invariably fail in the setting of most metastatic disease. This is changing with the advent of adaptive therapytherapeutic strategies specifically designed with a changing regimen prescribed: one that adapts to an evolving tumor 42 .…”

Section: Discussionmentioning

confidence: 99%

Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory

2020

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BACKGROUND: Recent clinical trials have shown that the adaptive drug therapy can be more efficient than a standard MTD-based policy in treatment of cancer patients. The adaptive therapy paradigm is not based on a preset schedule; instead, the doses are administered based on the current state of tumor. But the adaptive treatment policies examined so far have been largely ad hoc. In this paper we propose a method for systematically optimizing the rules of adaptive policies based on an Evolutionary Game Theory model of cancer dynamics.METHODS: 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 based on a mathematical model of tumor evolution.RESULTS: We compare adaptive/optimal treatment strategy with MTD-based treatment policy. We show that optimal treatment strategies can dramatically decrease the total amount of drugs prescribed as well as increase the fraction of initial tumour states from which the recovery is possible. We also examine the optimization tradeoffs between the total administered drugs and recovery time.CONCLUSIONS: The adaptive therapy combined with optimal control theory is a promising concept in the cancer treatment and should be integrated into clinical trial design. * Other tumor regimes (fully angiogenic and glycolyctic) also exist outside of this parameter range. They are less interesting from the point of view of treatment strategies, but we still consider them for the sake of completeness in Section 6.3 of Supplementary Materials.

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“…This would explain the low prevalence in the POT of the CDKN2A-loss clone. Moreover, evolutionary game theory has been proposed as a conceptual framework for adaptive therapy (Staňková et al, 2018). In this study we evaluated an additional level of complexity, where we use more than one drug to study evolutionary trade-offs and costs of resistance not just in the original drug-free environment, but also under the pressure of secondary drugs.…”

Section: Discussionmentioning

confidence: 99%

Exploiting evolutionary herding to control drug resistance in cancer

Acar

Nichol

Fernandez-Mateos

et al. 2019

Preprint

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Drug resistance mediated by clonal evolution is arguably the biggest problem in cancer therapy today. However, evolving resistance to one drug may come at a cost of decreased growth rate or increased sensitivity to another drug due to evolutionary trade-offs. This weakness can be exploited in the clinic using an approach called 'evolutionary herding' that aims at controlling the tumour cell population to delay or prevent resistance. However, recapitulating cancer evolutionary dynamics experimentally remains challenging. Here we present a novel approach for evolutionary herding based on a combination of single-cell barcoding, very large populations of 10 8 -10 9 cells grown without re-plating, longitudinal non-destructive monitoring of cancer clones, and mathematical modelling of tumour evolution. We demonstrate evolutionary herding in non-small cell lung cancer, showing that herding allows shifting the clonal composition of a tumour in our favour, leading to collateral drug sensitivity and proliferative fitness costs. Through genomic analysis and single-cell sequencing, we were also able to determine the mechanisms that drive such evolved sensitivity. Our approach allows modelling evolutionary trade-offs experimentally to test patient-specific evolutionary herding strategies that can potentially be translated into the clinic to control treatment resistance.Here, we present a novel experimental approach to study evolutionary herding quantitatively and demonstrate the evolutionary determinants of collateral drug sensitivity by clonal herding of cancer cell populations. Results Evolutionary herding of resistant cells through fitness landscapesThe relationship between heritable information, whether genetic or epigenetic, and the corresponding cellular phenotype, can be represented by the classical fitness landscape model

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Optimizing Cancer Treatment Using Game Theory

Cited by 164 publications

References 47 publications

Learning-accelerated Discovery of Immune-Tumour Interactions

Learning-accelerated Discovery of Immune-Tumour Interactions

Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory

Exploiting evolutionary herding to control drug resistance in cancer

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