101
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Mathematical modeling of tumor-immune cell interactions

      , ,
      Journal of Theoretical Biology
      Elsevier BV

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The anti-tumor activity of the immune system is increasingly recognized as critical for the mounting of a prolonged and effective response to cancer growth and invasion, and for preventing recurrence following resection or treatment. As the knowledge of tumor-immune cell interactions has advanced, experimental investigation has been complemented by mathematical modeling with the goal to quantify and predict these interactions. This succinct review offers an overview of recent tumor-immune continuum modeling approaches, highlighting spatial models. The focus is on work published in the past decade, incorporating one or more immune cell types and evaluating immune cell effects on tumor progression. Due to their relevance to cancer, the following immune cells and their combinations are described: macrophages, Cytotoxic T Lymphocytes, Natural Killer cells, dendritic cells, T regulatory cells, and CD4+ T helper cells. Although important insight has been gained from a mathematical modeling perspective, the development of models incorporating patient-specific data remains an important goal yet to be realized for potential clinical benefit.

          Related collections

          Author and article information

          Journal
          Journal of Theoretical Biology
          Journal of Theoretical Biology
          Elsevier BV
          00225193
          May 2019
          May 2019
          : 469
          : 47-60
          Article
          10.1016/j.jtbi.2019.03.002
          6579737
          30836073
          e8a2d657-3a30-424b-9d9e-71513745ab77
          © 2019

          https://www.elsevier.com/tdm/userlicense/1.0/

          History

          Comments

          Comment on this article