Epithelial/mesenchymal plasticity: how have quantitative mathematical models helped improve our understanding?

Mohit Kumar Jolly, Satyendra C. Tripathi, Jason A. Somarelli, Samir M. Hanash, Herbert Levine

Research output: Contribution to journalReview articlepeer-review

49 Scopus citations

Abstract

Phenotypic plasticity, the ability of cells to reversibly alter their phenotypes in response to signals, presents a significant clinical challenge to treating solid tumors. Tumor cells utilize phenotypic plasticity to evade therapies, metastasize, and colonize distant organs. As a result, phenotypic plasticity can accelerate tumor progression. A well-studied example of phenotypic plasticity is the bidirectional conversions among epithelial, mesenchymal, and hybrid epithelial/mesenchymal (E/M) phenotype(s). These conversions can alter a repertoire of cellular traits associated with multiple hallmarks of cancer, such as metabolism, immune evasion, invasion, and metastasis. To tackle the complexity and heterogeneity of these transitions, mathematical models have been developed that seek to capture the experimentally verified molecular mechanisms and act as ‘hypothesis-generating machines’. Here, we discuss how these quantitative mathematical models have helped us explain existing experimental data, guided further experiments, and provided an improved conceptual framework for understanding how multiple intracellular and extracellular signals can drive E/M plasticity at both the single-cell and population levels. We also discuss the implications of this plasticity in driving multiple aggressive facets of tumor progression.

Original languageEnglish (US)
Pages (from-to)739-754
Number of pages16
JournalMolecular oncology
Volume11
Issue number7
DOIs
StatePublished - Jul 2017

Keywords

  • circulating tumor cells
  • collective cell migration
  • epithelial-mesenchymal transition
  • hybrid epithelial/mesenchymal
  • mathematical modeling
  • stemness

ASJC Scopus subject areas

  • Molecular Medicine
  • Genetics
  • Oncology
  • Cancer Research

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