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Computational modelling of perivascular-niche dynamics for the optimization of treatment schedules for glioblastoma

Nature Biomedical Engineering volume 5, pages 346–359 (2021)Cite this article

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Abstract

Glioblastoma stem-like cells dynamically transition between a chemoradiation-resistant state and a chemoradiation-sensitive state. However, physical barriers in the tumour microenvironment restrict the delivery of chemotherapy to tumour compartments that are distant from blood vessels. Here, we show that a massively parallel computational model of the spatiotemporal dynamics of the perivascular niche that incorporates glioblastoma stem-like cells and differentiated tumour cells as well as relevant tissue-level phenomena can be used to optimize the administration schedules of concurrent radiation and temozolomide—the standard-of-care treatment for glioblastoma. In mice with platelet-derived growth factor (PDGF)-driven glioblastoma, the model-optimized treatment schedule increased the survival of the animals. For standard radiation fractionation in patients, the model predicts that chemotherapy may be optimally administered about one hour before radiation treatment. Computational models of the spatiotemporal dynamics of the tumour microenvironment could be used to predict tumour responses to a broader range of treatments and to optimize treatment regimens.

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Fig. 1: A computational model of the GBM microenvironment.
Fig. 2: Modelling GBM growth and treatment response.
Fig. 3: Prediction of the responses to different chemoradiation administration schedules.
Fig. 4: Prediction of GBM growth and treatment response.
Fig. 5: Validation of modelling predictions in a mouse model of GBM.
Fig. 6: Identification of a combination schedule in mice by optimizing the offset between chemotherapy and radiotherapy.
Fig. 7: Identification of an optimum schedule for human validation.

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are too large to be publicly shared, yet they are available for research purposes from the corresponding authors on reasonable request.

Code availability

The custom code used in this study is available at GitHub (https://github.com/arandles/chemoradiation) under the BSD-3-Clause open-source license.

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Acknowledgements

We acknowledge feedback and advice from members of the Michor laboratory, and D. Puleri for figure editing. We also acknowledge support from the Dana-Farber Physical Sciences Oncology Center (NIH U54CA193461, to F.M. and E.C.H.), the NIH Office of the Director (NIH, DP5OD019876, to A.R.), and the Lawrence Livermore National Laboratory Lawrence Fellowship (to A.R.). The mouse work was supported by P30 CA015704 at the Fred Hutchinson Cancer Research Center. This work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. Computing support for this work was provided by the Lawrence Livermore National Laboratory Institutional Computing Grand Challenges. F.M. acknowledges support from the Ludwig Center at Harvard.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Duke University, Durham, NC, USA

    Amanda Randles

  2. Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Hans-Georg Wirsching, Siobhan S. Pattwell & Eric C. Holland

  3. Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland

    Hans-Georg Wirsching

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

    Jamie A. Dean, Yu-Kang Cheng & Franziska Michor

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

    Jamie A. Dean & Franziska Michor

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

    Jamie A. Dean & Franziska Michor

  7. Department of Neurological Surgery, University of Washington, Seattle, WA, USA

    Samuel Emerson

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

    Franziska Michor

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

    Franziska Michor

  10. The Ludwig Center, Harvard University, Boston, MA, USA

    Franziska Michor

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  1. Amanda Randles
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Contributions

A.R., H.-G.W., E.C.H. and F.M. contributed to the design of the study. A.R., J.A.D. and Y.-K.C. performed the computational modelling. H.-G.W., S.E. and S.S.P. performed the mouse experiments. E.C.H. and F.M. supervised the study. All of the authors contributed to the writing of the paper.

Corresponding authors

Correspondence to Eric C. Holland or Franziska Michor.

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Randles, A., Wirsching, HG., Dean, J.A. et al. Computational modelling of perivascular-niche dynamics for the optimization of treatment schedules for glioblastoma. Nat Biomed Eng 5, 346–359 (2021). https://doi.org/10.1038/s41551-021-00710-3

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