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Computational approaches to modelling and optimizing cancer treatment

  • Review Article
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From Nature Reviews Bioengineering

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Abstract

Computational models can be applied to optimize treatment schedules and model treatment responses in cancer therapy. In this Review, we provide an overview of such computational approaches, including deterministic models, such as those based on ordinary and partial differential equations, stochastic models, spatially explicit agent-based approaches as well as control theory and machine learning methods. We discuss their advantages and current limitations in different scenarios. We outline how therapeutic decision-making can be aided by mathematical and computational approaches and how patient-specific responses can be assessed and incorporated into such methods. We also survey models that can incorporate adaptive changes throughout the course of treatment and discuss data and parameter estimation approaches. Finally, we highlight how such methods can lead to the identification of optimum treatment options for individual cancer and treatment types, and examine the challenges that remain to be addressed to enable the clinical translation of computational models in cancer therapy.

Key points

  • Computational approaches can be applied to describe the response of tumour cells to cancer treatment.

  • Such computational methods can be based on ordinary and partial differential equation modelling, stochastic modelling and spatially explicit agent-based models.

  • Adaptive treatment schedules and incorporation of patient-specific responses allow a personalized assessment of treatment options.

  • Control theory and machine learning methods, such as reinforcement learning, can be applied to design cancer treatment schedules.

  • Multimodal and longitudinal data sets could be integrated into patient-specific models to identify the best therapeutic options for individual patients.

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Fig. 1: ODE-based models.
Fig. 2: PDE models.
Fig. 3: Stochastic models.
Fig. 4: ABMs.
Fig. 5: OCT.

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Acknowledgements

The authors gratefully acknowledge support from the Dana-Farber Cancer Institute’s Center for Cancer Evolution.

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Authors and Affiliations

  1. Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA

    Thomas O. McDonald, Yu-Chen Cheng, Christopher Graser, Phillip B. Nicol, Daniel Temko & Franziska Michor

  2. Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA, USA

    Thomas O. McDonald & Franziska Michor

  3. Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Thomas O. McDonald, Yu-Chen Cheng, Phillip B. Nicol & Franziska Michor

  4. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA

    Thomas O. McDonald & Franziska Michor

  5. The Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Franziska Michor

  6. The Ludwig Center at Harvard, Boston, MA, USA

    Franziska Michor

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All authors contributed equally to the preparation of this manuscript.

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Correspondence to Franziska Michor.

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Competing interests

F.M. is a co-founder of and has equity in Harbinger Health, has equity in Zephyr AI, serves as a consultant for Harbinger Health and Zephyr AI and is on the board of directors of Exscientia Plc. F.M. declares that none of these relationships are directly or indirectly related to the content of this manuscript. All other authors declare no competing interests.

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McDonald, T.O., Cheng, YC., Graser, C. et al. Computational approaches to modelling and optimizing cancer treatment. Nat Rev Bioeng 1, 695–711 (2023). https://doi.org/10.1038/s44222-023-00089-7

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