Addressing current challenges in cancer immunotherapy with mathematical and computational modelling

The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and ther...

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Bibliographic Details
Published in:Journal of the Royal Society interface 2017-06, Vol.14 (131), p.20170150-20170150
Main Authors: Konstorum, Anna, Vella, Anthony T., Adler, Adam J., Laubenbacher, Reinhard C.
Format: Article
Language:English
Subjects:
Cancer
Cancer Immunotherapy
Cancer therapies
Computation
Computer applications
Computer Simulation
Design
Humans
Immune response
Immune system
Immunomodulation
Immunosuppression
Immunotherapy
Immunotherapy - methods
Mathematical analysis
Mathematical Modelling
Mathematical models
Models, Biological
Neoplasms - genetics
Neoplasms - therapy
Numerical analysis
Optimal Control
Optimization
Review
Review Articles
Reviews
Therapy
Toxicity
Tumor microenvironment
Tumors
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Summary:The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour 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 modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers 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 tumour–immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.
ISSN:1742-5689
1742-5662
DOI:10.1098/rsif.2017.0150