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      Combination therapy of cancer with cancer vaccine and immune checkpoint inhibitors: A mathematical model

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      PLoS ONE
      Public Library of Science

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          Abstract

          In this paper we consider a combination therapy of cancer. One drug is a vaccine which activates dendritic cells so that they induce more T cells to infiltrate the tumor. The other drug is a checkpoint inhibitor, which enables the T cells to remain active against the cancer cells. The two drugs are positively correlated in the sense that an increase in the amount of each drug results in a reduction in the tumor volume. We consider the question whether a treatment with combination of the two drugs at certain levels is preferable to a treatment by one of the drugs alone at ‘roughly’ twice the dosage level; if that is the case, then we say that there is a positive ‘synergy’ for this combination of dosages. To address this question, we develop a mathematical model using a system of partial differential equations. The variables include dendritic and cancer cells, CD4 + and CD8 + T cells, IL-12 and IL-2, GM-CSF produced by the vaccine, and a T cell checkpoint inhibitor associated with PD-1. We use the model to explore the efficacy of the two drugs, separately and in combination, and compare the simulations with data from mouse experiments. We next introduce the concept of synergy between the drugs and develop a synergy map which suggests in what proportion to administer the drugs in order to achieve the maximum reduction of tumor volume under the constraint of maximum tolerated dose.

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          Most cited references43

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          A methodology for performing global uncertainty and sensitivity analysis in systems biology.

          Accuracy of results from mathematical and computer models of biological systems is often complicated by the presence of uncertainties in experimental data that are used to estimate parameter values. Current mathematical modeling approaches typically use either single-parameter or local sensitivity analyses. However, these methods do not accurately assess uncertainty and sensitivity in the system as, by default, they hold all other parameters fixed at baseline values. Using techniques described within we demonstrate how a multi-dimensional parameter space can be studied globally so all uncertainties can be identified. Further, uncertainty and sensitivity analysis techniques can help to identify and ultimately control uncertainties. In this work we develop methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models. We compare two specific types of global sensitivity analysis indexes that have proven to be among the most robust and efficient. Through familiar and new examples of mathematical and computer models, we provide a complete methodology for performing these analyses, in both deterministic and stochastic settings, and propose novel techniques to handle problems encountered during these types of analyses.
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            Tumor-infiltrating dendritic cells in cancer pathogenesis.

            Dendritic cells (DCs) play a pivotal role in the tumor microenvironment, which is known to affect disease progression in many human malignancies. Infiltration by mature, active DCs into the tumors confers an increase in immune activation and recruitment of disease-fighting immune effector cells and pathways. DCs are the preferential target of infiltrating T cells. However, tumor cells have means of suppressing DC function or of altering the tumor microenvironment in such a way that immune-suppressive DCs are recruited. Advances in understanding these changes have led to promising developments in cancer-therapeutic strategies targeting tumor-infiltrating DCs to subdue their immunosuppressive functions and enhance their immune-stimulatory capacity.
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              Dendritic cells directly trigger NK cell functions: cross-talk relevant in innate anti-tumor immune responses in vivo.

              Cytotoxic T lymphocytes and natural killer cells are essential effectors of anti-tumor immune responses in vivo. Dendritic cells (DC) 'prime' tumor antigen-specific cytotoxic T lymphocytes; thus, we investigated whether DC might also trigger the innate, NK cell-mediated anti-tumor immunity. In mice with MHC class I-negative tumors, adoptively transferred- or Flt3 ligand-expanded DC promoted NK cell-dependent anti-tumor effects. In vitro studies demonstrated a cell-to-cell contact between DC and resting NK cells that resulted in a substantial increase in both NK cell cytolytic activity and IFN-gamma production. Thus, DC are involved in the interaction between innate and adaptive immune responses.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2017
                25 May 2017
                : 12
                : 5
                : e0178479
                Affiliations
                [1 ]Institute for Mathematical Sciences, Renmin University of China, Beijing, P. R. China
                [2 ]Mathematical Biosciences Institute & Department of Mathematics, Ohio State University, Columbus, OH, United States of America
                University of Queensland Diamantina Institute, AUSTRALIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: AF XL.

                • Data curation: AF XL.

                • Formal analysis: AF XL.

                • Funding acquisition: AF.

                • Investigation: AF XL.

                • Methodology: AF XL.

                • Project administration: AF XL.

                • Resources: AF XL.

                • Software: AF XL.

                • Validation: AF XL.

                • Visualization: AF XL.

                • Writing – original draft: AF XL.

                • Writing – review & editing: AF XL.

                Author information
                http://orcid.org/0000-0002-2764-8937
                Article
                PONE-D-16-50091
                10.1371/journal.pone.0178479
                5444846
                28542574
                ac069870-35c8-4f63-aa8c-19bc42c725ca
                © 2017 Lai, Friedman

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 19 December 2016
                : 12 May 2017
                Page count
                Figures: 8, Tables: 3, Pages: 24
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: 0931642
                Award Recipient :
                This work is supported by the Mathematical Biosciences Institute and the National Science Foundation (Grant DMS 0931642), and the Renmin University of China and the International Postdoctoral Exchange Fellowship Program 2016 by the Office of China Postdoctoral Council.
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