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Addressing current challenges in cancer immunotherapy with mathematical and computational modeling

Anna Konstorum, Anthony T. Vella, View ORCID ProfileAdam J. Adler, View ORCID ProfileReinhard Laubenbacher
doi: https://doi.org/10.1101/146902
Anna Konstorum
1 Center for Quzantitative Medicine, UConn Health, Farmington, CT
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Anthony T. Vella
2 Department of Immunology, UConn Health, Farmington, CT
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Adam J. Adler
2 Department of Immunology, UConn Health, Farmington, CT
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Reinhard Laubenbacher
3 Jackson Laboratory for Genomic Medicine, Farmington, CT
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  • For correspondence: laubenbacher@uchc.edu
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Abstract

The goal of cancer immunotherapy is to boost a patient’s immune response to a tumor. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumor microenvironment, immune-modulating effects of conventional treatments, and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modeling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumor classification, optimal treatment scheduling, and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modelers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumor-immune biology. We conclude the review with recommendations for modelers both with respect to methodology and biological direction that might help keep modelers at the forefront of cancer immunotherapy development.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 09, 2017.
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Addressing current challenges in cancer immunotherapy with mathematical and computational modeling
Anna Konstorum, Anthony T. Vella, Adam J. Adler, Reinhard Laubenbacher
bioRxiv 146902; doi: https://doi.org/10.1101/146902
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Addressing current challenges in cancer immunotherapy with mathematical and computational modeling
Anna Konstorum, Anthony T. Vella, Adam J. Adler, Reinhard Laubenbacher
bioRxiv 146902; doi: https://doi.org/10.1101/146902

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