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Mathematical modeling of cancer immunotherapy for personalized clinical translation

Nature Computational Science volume 2, pages 785–796 (2022)Cite this article

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Abstract

Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients’ lives. We discuss how researchers are integrating experimental and clinical data to fully inform models so that they can be applied for clinical predictions, and present the challenges that remain to be overcome if widespread clinical adaptation is to be realized. Lastly, we discuss the most promising future applications and areas that are expected to be the focus of extensive upcoming modeling studies.

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Fig. 1: Selected key tumor–immune interaction processes for consideration in model design.
Fig. 2: Overview of cancer immunotherapy modeling applications in the four key research areas examined in this Review.
Fig. 3: Conceptual design of a strategy for simplifying mathematical models for clinical application by accounting for current advances in both AI and cancer biology.

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Acknowledgements

This research has been supported in part by the National Science Foundation Grant DMS-1930583 (V.C. and Z.W.), the National Institutes of Health (NIH) Grants 1R01CA253865 (V.C. and Z.W.), 1R01CA226537 (R.P., W.A., V.C. and Z.W.), 1R01CA222007 (V.C. and Z.W.), 1R01AI165372 (Z.W.), 1R01DK132104 (Z.W.) and 1R01DK133610 (Z.W.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA

    Joseph D. Butner, Prashant Dogra, Vittorio Cristini & Zhihui Wang

  2. Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Caroline Chung

  3. Rutgers Cancer Institute of New Jersey, Newark, NJ, USA

    Renata Pasqualini & Wadih Arap

  4. Department of Radiation Oncology, Division of Cancer Biology, Rutgers New Jersey Medical School, Newark, NJ, USA

    Renata Pasqualini

  5. Department of Medicine, Division of Hematology/Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA

    Wadih Arap

  6. Department of Mathematics, University of California at Irvine, Irvine, CA, USA

    John Lowengrub

  7. Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA

    Vittorio Cristini & Zhihui Wang

  8. Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Vittorio Cristini

  9. Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA

    Vittorio Cristini

  10. Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA

    Zhihui Wang

Authors
  1. Joseph D. Butner
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  2. Prashant Dogra
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  3. Caroline Chung
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  4. Renata Pasqualini
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  5. Wadih Arap
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  6. John Lowengrub
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  7. Vittorio Cristini
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  8. Zhihui Wang
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Contributions

Z.W. conceived and coordinated this work. J.D.B. and Z.W. drafted the structure of the paper, reviewed the literature and contributed to figures. P.D., C.C., R.P,, W.A. and V.C. provided critical feedback on figures. All authors contributed to the writing and revision of the paper.

Corresponding author

Correspondence to Zhihui Wang.

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The authors declare no competing interests.

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Nature Computational Science thanks Walter Koch and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ananya Rastogi, in collaboration with the Nature Computational Science team.

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Butner, J.D., Dogra, P., Chung, C. et al. Mathematical modeling of cancer immunotherapy for personalized clinical translation. Nat Comput Sci 2, 785–796 (2022). https://doi.org/10.1038/s43588-022-00377-z

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